PR-284: End-to-End Object Detection with Transformers(DETR)Jinwon Lee
TensorFlow Korea 논문읽기모임 PR12 284번째 논문 review입니다.
이번 논문은 Facebook에서 나온 DETR(DEtection with TRansformer) 입니다.
arxiv-sanity에 top recent/last year에서 가장 상위에 자리하고 있는 논문이기도 합니다(http://www.arxiv-sanity.com/top?timefilter=year&vfilter=all)
최근에 ICLR 2021에 submit된 ViT로 인해서 이제 Transformer가 CNN을 대체하는 것 아닌가 하는 얘기들이 많이 나오고 있는데요, 올 해 ECCV에 발표된 논문이고 feature extraction 부분은 CNN을 사용하긴 했지만 transformer를 활용하여 효과적으로 Object Detection을 수행하는 방법을 제안한 중요한 논문이라고 생각합니다. 이 논문에서는 detection 문제에서 anchor box나 NMS(Non Maximum Supression)와 같은 heuristic 하고 미분 불가능한 방법들이 많이 사용되고, 이로 인해서 유독 object detection 문제는 딥러닝의 철학인 end-to-end 방식으로 해결되지 못하고 있음을 지적하고 있습니다. 그 해결책으로 bounding box를 예측하는 문제를 set prediction problem(중복을 허용하지 않고, 순서에 무관함)으로 보고 transformer를 활용한 end-to-end 방식의 알고리즘을 제안하였습니다. anchor box도 필요없고 NMS도 필요없는 DETR 알고리즘의 자세한 내용이 알고싶으시면 영상을 참고해주세요!
영상링크: https://youtu.be/lXpBcW_I54U
논문링크: https://arxiv.org/abs/2005.12872
Prediction and planning for self driving at waymoYu Huang
ChauffeurNet: Learning To Drive By Imitating The Best Synthesizing The Worst
Multipath: Multiple Probabilistic Anchor Trajectory Hypotheses For Behavior Prediction
VectorNet: Encoding HD Maps And Agent Dynamics From Vectorized Representation
TNT: Target-driven Trajectory Prediction
Large Scale Interactive Motion Forecasting For Autonomous Driving : The Waymo Open Motion Dataset
Identifying Driver Interactions Via Conditional Behavior Prediction
Peeking Into The Future: Predicting Future Person Activities And Locations In Videos
STINet: Spatio-temporal-interactive Network For Pedestrian Detection And Trajectory Prediction
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
PR-284: End-to-End Object Detection with Transformers(DETR)Jinwon Lee
TensorFlow Korea 논문읽기모임 PR12 284번째 논문 review입니다.
이번 논문은 Facebook에서 나온 DETR(DEtection with TRansformer) 입니다.
arxiv-sanity에 top recent/last year에서 가장 상위에 자리하고 있는 논문이기도 합니다(http://www.arxiv-sanity.com/top?timefilter=year&vfilter=all)
최근에 ICLR 2021에 submit된 ViT로 인해서 이제 Transformer가 CNN을 대체하는 것 아닌가 하는 얘기들이 많이 나오고 있는데요, 올 해 ECCV에 발표된 논문이고 feature extraction 부분은 CNN을 사용하긴 했지만 transformer를 활용하여 효과적으로 Object Detection을 수행하는 방법을 제안한 중요한 논문이라고 생각합니다. 이 논문에서는 detection 문제에서 anchor box나 NMS(Non Maximum Supression)와 같은 heuristic 하고 미분 불가능한 방법들이 많이 사용되고, 이로 인해서 유독 object detection 문제는 딥러닝의 철학인 end-to-end 방식으로 해결되지 못하고 있음을 지적하고 있습니다. 그 해결책으로 bounding box를 예측하는 문제를 set prediction problem(중복을 허용하지 않고, 순서에 무관함)으로 보고 transformer를 활용한 end-to-end 방식의 알고리즘을 제안하였습니다. anchor box도 필요없고 NMS도 필요없는 DETR 알고리즘의 자세한 내용이 알고싶으시면 영상을 참고해주세요!
영상링크: https://youtu.be/lXpBcW_I54U
논문링크: https://arxiv.org/abs/2005.12872
Prediction and planning for self driving at waymoYu Huang
ChauffeurNet: Learning To Drive By Imitating The Best Synthesizing The Worst
Multipath: Multiple Probabilistic Anchor Trajectory Hypotheses For Behavior Prediction
VectorNet: Encoding HD Maps And Agent Dynamics From Vectorized Representation
TNT: Target-driven Trajectory Prediction
Large Scale Interactive Motion Forecasting For Autonomous Driving : The Waymo Open Motion Dataset
Identifying Driver Interactions Via Conditional Behavior Prediction
Peeking Into The Future: Predicting Future Person Activities And Locations In Videos
STINet: Spatio-temporal-interactive Network For Pedestrian Detection And Trajectory Prediction
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
Multi-object tracking is a computer vision task which can track objects belonging to different categories, such as cars, pedestrians and animals by analyzing the videos.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...Joonhyung Lee
A presentation introducting DeepLab V3+, the state-of-the-art architecture for semantic segmentation. It also includes detailed descriptions of how 2D multi-channel convolutions function, as well as giving a detailed explanation of depth-wise separable convolutions.
Object detection is a central problem in computer vision and underpins many applications from medical image analysis to autonomous driving. In this talk, we will review the basics of object detection from fundamental concepts to practical techniques. Then, we will dive into cutting-edge methods that use transformers to drastically simplify the object detection pipeline while maintaining predictive performance. Finally, we will show how to train these models at scale using Determined’s integrated deep learning platform and then serve the models using MLflow.
What you will learn:
Basics of object detection including main concepts and techniques
Main ideas from the DETR and Deformable DETR approaches to object detection
Overview of the core capabilities of Determined’s deep learning platform, with a focus on its support for effortless distributed training
How to serve models trained in Determined using MLflow
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Crime rate analysis using k nn in python
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
PR-302: NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisHyeongmin Lee
드디어 PR12 Season 4가 시작되었습니다! 제가 이번 시즌에서 발표하게 된 첫 논문은 ""NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"라는 논문입니다. View Synthesis라는 Task는 몇 개의 시점에서 대상을 찍은 영상이 주어지면 주어지지 않은 위치와 방향에서 바라본 대상의 영상을 합성해내는 기술입니다. 이를 위해서 본 논문에서는 대상의 3D 정보를 통째로 Neural Network가 외우게 하는 방법을 선택했는데요, 이 방식은 Implicit Neural Representation이라는 이름으로 유명해지고 있는 추세고, 2D 이미지에 대해서도 적용하려는 접근들이 늘고 있습니다.
영상 링크: https://youtu.be/zkeh7Tt9tYQ
논문 링크: https://arxiv.org/abs/2003.08934
Multi-object tracking is a computer vision task which can track objects belonging to different categories, such as cars, pedestrians and animals by analyzing the videos.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...Joonhyung Lee
A presentation introducting DeepLab V3+, the state-of-the-art architecture for semantic segmentation. It also includes detailed descriptions of how 2D multi-channel convolutions function, as well as giving a detailed explanation of depth-wise separable convolutions.
Object detection is a central problem in computer vision and underpins many applications from medical image analysis to autonomous driving. In this talk, we will review the basics of object detection from fundamental concepts to practical techniques. Then, we will dive into cutting-edge methods that use transformers to drastically simplify the object detection pipeline while maintaining predictive performance. Finally, we will show how to train these models at scale using Determined’s integrated deep learning platform and then serve the models using MLflow.
What you will learn:
Basics of object detection including main concepts and techniques
Main ideas from the DETR and Deformable DETR approaches to object detection
Overview of the core capabilities of Determined’s deep learning platform, with a focus on its support for effortless distributed training
How to serve models trained in Determined using MLflow
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Crime rate analysis using k nn in python
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
PR-302: NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisHyeongmin Lee
드디어 PR12 Season 4가 시작되었습니다! 제가 이번 시즌에서 발표하게 된 첫 논문은 ""NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"라는 논문입니다. View Synthesis라는 Task는 몇 개의 시점에서 대상을 찍은 영상이 주어지면 주어지지 않은 위치와 방향에서 바라본 대상의 영상을 합성해내는 기술입니다. 이를 위해서 본 논문에서는 대상의 3D 정보를 통째로 Neural Network가 외우게 하는 방법을 선택했는데요, 이 방식은 Implicit Neural Representation이라는 이름으로 유명해지고 있는 추세고, 2D 이미지에 대해서도 적용하려는 접근들이 늘고 있습니다.
영상 링크: https://youtu.be/zkeh7Tt9tYQ
논문 링크: https://arxiv.org/abs/2003.08934
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
The intensive development of traffic engineering and technologies that are integrated into
vehicles, roads and their surroundings, bring opportunities of real time transport mobility
modeling. Based on such model it is then possible to establish a predictive layer that is capable
of predicting short and long term traffic flow behavior. It is possible to create the real time
model of traffic mobility based on generated data. However, data may have different
geographical, temporal or other constraints, or failures. It is therefore appropriate to develop
tools that artificially create missing data, which can then be assimilated with real data. This
paper presents a mechanism describing strategies of generating artificial data using
microsimulations. It describes traffic microsimulation based on our solution of multiagent
framework over which a system for generating traffic data is built. The system generates data of
a structure corresponding to the data acquired in the real world.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
A Study of Mobile User Movements Prediction Methods IJECEIAES
For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and locationbased services are some of the fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.
Mobility models for delay tolerant network a surveyijwmn
Delay Tolerant Network (DTN) is an emerging networking technology that is widely used in the
environment where end-to-end paths do not exist. DTN follows store-carry-forward mechanism to route
data. This mechanism exploits the mobility of nodes and hence the performances of DTN routing and
application protocols are highly dependent on the underlying mobility of nodes and its characteristics.
Therefore, suitable mobility models are required to be incorporated in the simulation tools to evaluate DTN
protocols across many scenarios. In DTN mobility modelling literature, a number of mobility models have
been developed based on synthetic theory and real world mobility traces. Furthermore, many researchers
have developed specific application oriented mobility models. All these models do not provide accurate
evaluation in the all scenarios. Therefore, model selection is an important issue in DTN protocol
simulation. In this study, we have summarized various widely used mobility models and made a comparison
of their performances. Finally, we have concluded with future research directions in mobility modelling for
DTN simulation.
Advancement in VANET Routing by Optimize the Centrality with ANT Colony Approachijceronline
In a wireless ad hoc network, an opportunistic routing strategy is a strategy where there is no predefined rule for choosing the next node to destination (as it is the case in conventional schemes such as OLSR, DSR or even Geo-Routing). A popular example of opportunistic routing is the “delay tolerant” forwarding to VANET network when a direct path to destination does not exist. Conventional routing in this case would just “drop” the packet. With opportunistic routing, a node acts upon the available information, In this thesis optimize the routing by centrality information then refine by ant colony metaheuristics. In this method validate our approach on different parameter like overhead, throughput.
Similar to Pedestrian behavior/intention modeling for autonomous driving V (20)
Application of Foundation Model for Autonomous DrivingYu Huang
Since DARPA’s Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Recently powered by large language models (LLMs), chat systems, such as chatGPT and PaLM, emerge and rapidly become a promising direction to achieve artificial general intelligence (AGI) in natural language processing (NLP). There comes a natural thinking that we could employ these abilities to reformulate autonomous driving. By combining LLM with foundation models, it is possible to utilize the human knowledge, commonsense and reasoning to rebuild autonomous driving systems from the current long-tailed AI dilemma. In this paper, we investigate the techniques of foundation models and LLMs applied for autonomous driving, categorized as simulation, world model, data annotation and planning or E2E solutions etc.
Fisheye based Perception for Autonomous Driving VIYu Huang
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
FisheyeDistanceNet++: Self-Supervised Fisheye Distance Estimation with Self-Attention, Robust Loss Function and Camera View Generalization
An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras
RoadEdgeNet: Road Edge Detection System Using Surround View Camera Images
Fisheye/Omnidirectional View in Autonomous Driving VYu Huang
Road-line detection and 3D reconstruction using fisheye cameras
• Vehicle Re-ID for Surround-view Camera System
• SynDistNet: Self-Supervised Monocular Fisheye Camera Distance
Estimation Synergized with Semantic Segmentation for Autonomous
Driving
• Universal Semantic Segmentation for Fisheye Urban Driving Images
• UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a
Generic Framework for Handling Common Camera Distortion Models
• OmniDet: Surround View Cameras based Multi-task Visual Perception
Network for Autonomous Driving
• Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving
Fisheye/Omnidirectional View in Autonomous Driving IVYu Huang
FisheyeMultiNet: Real-time Multi-task Learning Architecture for
Surround-view Automated Parking System
• Generalized Object Detection on Fisheye Cameras for Autonomous
Driving: Dataset, Representations and Baseline
• SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for
Autonomous Driving
• Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors
for the ADAS
Autonomous driving for robotaxi, like perception, prediction, planning, decision making and control etc. As well as simulation, visualization and data closed loop etc.
LiDAR in the Adverse Weather: Dust, Snow, Rain and Fog (2)Yu Huang
Canadian Adverse Driving Conditions Dataset, 2020, 2
Deep multimodal sensor fusion in unseen adverse weather, 2020, 8
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather, 2021, 4
Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection, 2021, 7
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather, 2021, 8
DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather, 2021, 9
Scenario-Based Development & Testing for Autonomous DrivingYu Huang
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World, 2020
A Scenario-Based Development Framework for Autonomous Driving, 2020
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving, 2020
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction, 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles, 2021
Systems Approach to Creating Test Scenarios for Automated Driving Systems, Reliability Engineering and System Safety (215), 2021
How to Build a Data Closed-loop Platform for Autonomous Driving?Yu Huang
Introduction;
data driven models for autonomous driving;
cloud computing infrastructure and big data processing;
annotation tools for training data;
large scale model training platform;
model testing and verification;
related machine learning techniques;
Conclusion.
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
RGGNet: Tolerance Aware LiDAR-Camera Online Calibration with Geometric Deep Learning and Generative Model
CalibRCNN: Calibrating Camera and LiDAR by Recurrent Convolutional Neural Network and Geometric Constraints
LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network
CFNet: LiDAR-Camera Registration Using Calibration Flow Network
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
2. Outline
• Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and
Abnormal Event Detection (17.2.18)
• Group LSTM: Group Trajectory Prediction in Crowded Scenarios (ECCV2018 workshop)
• Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention
Networks (7.17)
• The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic
Spatiotemporal Graphs (8.23)
• Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM (8.23)
• STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction (ICCV19)
• Neighbourhood Context Embeddings in Deep Inverse Reinforcement Learning for
Predicting Pedestrian Motion Over Long Time Horizons (ICCV19)
• GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction (3.26)
• Recursive Social Behavior Graph for Trajectory Prediction (4.22)
3. Soft + Hardwired Attention: An LSTM Framework for Human
Trajectory Prediction and Abnormal Event Detection
• As humans we possess an intuitive ability for navigation which we master through years
of practice; however existing approaches to model this trait for diverse tasks including
monitoring pedestrian flow and detecting abnormal events have been limited by using a
variety of hand-crafted features.
• Recent research in the area of deep- learning has demonstrated the power of learning
features directly from the data; and related research in recurrent neural networks has
shown exemplary results in sequence- to-sequence problems such as neural machine
translation and neural image caption generation.
• Motivated by these approaches, a method to predict the future motion of a pedestrian
given a short history of their, and their neighbours, past behaviour.
• The novelty of the method is the combined attention model which utilises both “soft
attention” as well as “hard-wired” attention in order to map the trajectory information
from the local neighbourhood to the future positions of the pedestrian of interest.
• How a simple approximation of attention weights (i.e. hard-wired) can be merged
together with soft attention weights in order to make our model applicable for
challenging real world scenarios with hundreds of neighbours.
4. Soft + Hardwired Attention: An LSTM Framework for Human
Trajectory Prediction and Abnormal Event Detection
A scene (on the left): The trajectory of the pedestrian of interest is shown in green, and has two neighbours (shown
in purple) to the left, one in front and none on right. Neighbourhood encoding scheme (on the right): Trajectory
information is encoded with LSTM encoders. A soft attention context vector is used to embed the trajectory
information from the pedestrian of interest, and a hardwired attention context vector is used for neighbouring
trajectories. In order to generate soft attention vector, use a soft attention function. The merged context vector is
then used to predict the future trajectory for the pedestrian of interest (shown in red).
5. Soft + Hardwired Attention: An LSTM Framework for Human
Trajectory Prediction and Abnormal Event Detection
The Soft + Hardwired Attention model. utilise the trajectory information from both the pedestrian of
interest and the neighbouring trajectories. embed the trajectory information from the pedestrian of
interest with the soft attention context vector, while neighbouring trajectories are embedded with the aid
of a hardwired attention context vector. In order to generate soft attention context vector, use a soft
attention function. Then the merged context vector, is used to predict the future state
6. Soft + Hardwired Attention: An LSTM Framework for Human
Trajectory Prediction and Abnormal Event Detection
7. Group LSTM: Group Trajectory Prediction in Crowded Scenarios
• The analysis of crowded scenes is one of the most challenging scenarios in visual
surveillance, and a variety of factors need to be taken into account, such as the structure
of the environments, and the presence of mutual occlusions and obstacles.
• Traditional prediction methods (such as RNN, LSTM, VAE, etc.) focus on anticipating
individual’s future path based on the precise motion history of a pedestrian.
• However, since tracking algorithms are generally not reliable in highly dense scenes,
these methods are not easily applicable in real environments.
• Nevertheless, it is very common that people (friends, couples, family members, etc.)
tend to exhibit coherent motion patterns.
• Motivated by this phenomenon, an approach to predict future trajectories in crowded
scenes, at the group level.
• First, by exploiting the motion coherency, cluster trajectories that have similar motion
trends.
• In this way, pedestrians within the same group can be well segmented.
• Then, an improved social-LSTM is adopted for future path prediction.
8. Group LSTM: Group Trajectory Prediction in Crowded Scenarios
i
Representation of the Social hidden-state tensor. The black dot represents the pedestrian of interest. Other
pedestrians are shown in different color codes, namely green for pedestrians belonging to the same set, and
red for pedestrians belonging to a different set. The neighborhood of pedestrian of interest is described by
N0 × N0 cells, which preserves the spatial information by pooling spatially adjacent neighbors. Pedestrians
belonging to the same set are not used for the final computation of the pooling layer.
9. Group LSTM: Group Trajectory Prediction in Crowded Scenarios
The figure represents the chain structure of the LSTM network between two consecutive
time steps. At each time step, the inputs of the LSTM cell are the previous position and
the Social pooling tensor Ht. The output of the LSTM cell is the current position.
11. Social-BiGAT: Multimodal Trajectory Forecasting using
Bicycle-GAN and Graph Attention Networks
• Predicting the future trajectories of multiple interacting agents in a scene has become an
increasingly important problem for many different applications ranging from control of
autonomous vehicles and social robots to security and surveillance.
• This problem is compounded by the presence of social interactions between humans and
their physical interactions with the scene.
• While the existing literature has explored some of these cues, they mainly ignored the
multimodal nature of each human’s future trajectory.
• Social-BiGAT, a graph-based generative adversarial network that generates realistic,
multimodal trajectory predictions by better modelling the social interactions of
pedestrians in a scene.
• Based on a graph attention network (GAT) that learns reliable feature representations
that encode the social interactions between humans in the scene, and a recurrent
encoder-decoder architecture that is trained adversarially to predict, based on the
features, the humans’ paths.
• The multimodal nature of the prediction by forming a reversible transformation between
each scene and its latent noise vector, as in Bicycle-GAN.
12. Social-BiGAT: Multimodal Trajectory Forecasting using
Bicycle-GAN and Graph Attention Networks
Architecture for the Social-BiGAT model. The
model consists of a single generator, two
discriminators (one at local pedestrian scale,
and one at global scene scale), and a latent
encoder that learns noise from scenes. The
model makes use of a graph attention network
(GAT) and self-attention on an image to
consider the social and physical features of a
scene.
13. Social-BiGAT: Multimodal Trajectory Forecasting using
Bicycle-GAN and Graph Attention Networks
Training process for the Social-BiGAT model. Teach the generator and discriminators using traditional
adversarial learning techniques, with an additional L2 loss on generated samples to encourage
consistency. Further train the latent encoder by ensuring it can recreate noise passed into the generator,
and by making sure it mirrors a normal distribution.
15. The Trajectron: Probabilistic Multi-Agent Trajectory
Modeling With Dynamic Spatiotemporal Graphs
• Developing safe human-robot interaction systems is a necessary step towards the
widespread integration of autonomous agents in society.
• A key component of such systems is the ability to reason about the many potential
futures (e.g. trajectories) of other agents in the scene.
• Trajectron, a graph-structured model that predicts many potential future trajectories of
multiple agents simultaneously in both highly dynamic and multi- modal scenarios (i.e.
where the number of agents in the scene is time-varying and there are many possible
highly- distinct futures for each agent).
• It combines tools from recurrent sequence modeling and variational deep generative
modeling to produce a distribution of future trajectories for each agent in a scene.
• Test the performance of the model on several datasets, obtaining state-of-the-art results
on standard trajectory prediction metrics as well as introducing a new metric for
comparing models that output distributions.
16. The Trajectron: Probabilistic Multi-Agent Trajectory
Modeling With Dynamic Spatiotemporal Graphs
Top: An example graph with four nodes. a is the
modeled node and is of type T3. It has three
neighbors: b of type T1, c of type T2, and d of type
T1. Here, c is about to connect with a. Bottom: The
corresponding architecture for node a.
Overall, the Trajectron employs a hybrid edge
combination scheme combining aspects of Social
Attention and the Structural-RNN.
17. The Trajectron: Probabilistic Multi-Agent Trajectory
Modeling With Dynamic Spatiotemporal Graphs
• Trajectron combines elements of variational deep generative models (in particular, CVAEs),
recurrent sequence models (LSTMs), and dynamic spatiotemporal graphical structures to produce
high-quality multimodal trajectories that models/predicts future behaviors of multiple humans.
• Trajectron actually models a human’s velocity, which is then numerically integrated to produce
spatial trajectories.
• Build a graph G = (V , E ) representing the scene with nodes representing agents and edges based
on agents’ spatial proximity.
• Node History Encoder (NHE) to encode a node’s state history;
• Edge Encoders (EEs) to incorporate influence from neighboring nodes.
• With the previous outputs in hand, form a concatenated representation which then
parameterizes the recognition and prior distributions in the CVAE framework.
19. Trajectory Prediction by Coupling Scene-LSTM
with Human Movement LSTM
• A trajectory prediction system that incorporates the scene information (Scene-
LSTM) as well as individual pedestrian movement (Pedestrian-LSTM) trained
simultaneously within static crowded scenes.
• Superimpose a two-level grid structure (grid cells and subgrids) on the scene to
encode spatial granularity plus common human movements.
• The Scene-LSTM captures the commonly traveled paths that can be used to
significantly influence the accuracy of human trajectory prediction in local areas
(i.e. grid cells).
• Further design scene data filters, consisting of a hard filter and a soft filter, to
select the relevant scene information in a local region when necessary and
combine it with Pedestrian-LSTM for forecasting a pedestrian’s future locations.
• The experimental results on several publicly available datasets demonstrate that
it produces more accurate predicted trajectories in different scene contexts.
20. Trajectory Prediction by Coupling Scene-LSTM
with Human Movement LSTM
Scene-LSTM learns common human movements on a two-level
grid structure. The common human movement is filtered and
used in combination with individual movement (Pedestrian-
LSTM) to predict a pedestrian’s future locations.
21. Trajectory Prediction by Coupling Scene-LSTM
with Human Movement LSTM
The system consists of three main modules: Pedestrian Movement (PM), Scene Data (SD) and Scene Data Filter
(SDF). PM models the individual movement of pedestrians. SD encodes common human movements in each
grid cell. SDF selects relevant scene data to update the Pedestrian-LSTM, which is used to predict the future
locations. ⊗ denotes elementwise multiplication. ⊕ denotes vector addition. hi and hsare the hidden states of
Pedestrian-LSTM and Scene-LSTM, respectively.
22. Trajectory Prediction by Coupling Scene-LSTM
with Human Movement LSTM
Illustrations of the hard filter, which determines whether the scene data should be applied in predicting the
future locations of a pedestrian. (a) the frame image is first divided into n × n grid cells (n = 4 in this example)
to capture all human movements in each grid cell; (b) & (c) only non-linear grid cells are selected for further
processing at the subgrid level; the scene data is not applied for pedestrians in the linear grid cell; (d) a non-
linear grid cell is further divided into m × m subgrids (m = 4) and each trajectory is parsed into subgrid paths;
(e) the common subgrids, occupied by common subgrid paths; (f) at prediction time, the decision of use/not
use scene data depends on the current location of each pedestrian. If the pedestrian’s current location is in the
common subgrids, the scene data is used (red pedestrian); otherwise, it is not used (green pedestrian).
23. Trajectory Prediction by Coupling Scene-LSTM
with Human Movement LSTM
Illustrations of the soft filter. The relevant information of scene data (i.e. Scene-LSTM) is selected using
each pedestrians walking behavior. The filtered grid-cell memory of each pedestrian is then used in
combination with pedestrian movements (Pedestrian-LSTM) to predict the future trajectories.
25. STGAT: Modeling Spatial-Temporal Interactions for
Human Trajectory Prediction
• Human trajectory prediction is challenging and critical in various applications (e.g., autonomous
vehicles and social robots).
• Because of the continuity and foresight of the pedestrian movements, the moving pedestrians in
crowded spaces will consider both spatial and temporal interactions to avoid future collisions.
• However, most of the existing methods ignore the temporal correlations of interactions with
other pedestrians involved in a scene.
• Spatial-Temporal Graph Attention network (STGAT), based on a sequence-to-sequence
architecture to predict future trajectories of pedestrians.
• Besides the spatial interactions captured by the graph attention mechanism at each time-step,
adopt an extra LSTM to encode the temporal correlations of interactions.
• Test on two publicly available crowd datasets (ETH and UCY) and produces more “socially”
plausible trajectories for pedestrians.
26. STGAT: Modeling Spatial-Temporal Interactions for
Human Trajectory Prediction
The architecture of the STGAT model. The framework is based on seq2seq model and consists of 3 parts: Encoder,
Intermediate State and Decoder. The Encoder module includes three components: 2 types of LSTMs and Graph
Attention Network (GAT) . The Intermediate State encapsulates the spatial and temporal information of all
observed trajectories. The Decoder module generates the future trajectories based on Intermediate State.
29. Neighbourhood Context Embeddings in Deep Inverse Reinforcement
Learning for Predicting Pedestrian Motion Over Long Time Horizons
• Despite the fact that Deep Inverse Reinforcement Learning (D-IRL) based modelling
paradigms offer flexibility and robustness when anticipating human behaviour across
long time horizons, compared to their supervised learning counterparts, no existing
state-of-the-art D-IRL methods consider path planning in situations where there are
multiple moving pedestrians in the environment.
• To address this, a recurrent neural network based method for embedding pedestrian
dynamics in a D-IRL setting, where there are multiple moving agents.
• Capture the motion of the pedestrian of interest as well as the motion of other
pedestrians in the neighbourhood through Long-Short-Term Memory networks.
• The neighbourhood dynamics are encoded into a feature map, preserving the spatial
integrity of the observed trajectories.
• Utilising the maximum-entropy based non-linear inverse reinforcement learning
framework, map these features to a reward map.
• The importance of capturing the dynamic evolution of the environment using the
embedding scheme.
30. Neighbourhood Context Embeddings in Deep Inverse Reinforcement
Learning for Predicting Pedestrian Motion Over Long Time Horizons
The architecture used to embed the
neighbourhood context: The trajectory of the
pedestrian of interest is shown in blue, with
three neighbours shown in green. Heading
directions are indicated with circles. encode
the trajectories using LSTMs where soft
attention is utilised to embed the information
from the pedestrian of interest and the
neighbours use hard-wired attention. Next a
feature map is generated to embed this
information spatially, based on the cartesian
points of each trajectory.
31. Neighbourhood Context Embeddings in Deep Inverse Reinforcement
Learning for Predicting Pedestrian Motion Over Long Time Horizons
The architecture of the four layer fully convolution network used to
map the feature map G to the reward map R. The first three layers
contain 32, 1 × 1 convolution kernels with a ReLU activation, and
the final layer contains 1, 1 × 1 convolution kernel.
The learned reward map covers all
the areas of the environment,
encapsulating structural factors such
as buildings and pathways that
influence pedestrian behaviour.
32. Neighbourhood Context Embeddings in Deep Inverse Reinforcement
Learning for Predicting Pedestrian Motion Over Long Time Horizons
33. GraphTCN: Spatio-Temporal Interaction Modeling
for Human Trajectory Prediction
• Trajectory prediction is a fundamental and challenging task to forecast the future path of the
agents in autonomous applications with multi-agent interaction, where the agents need to
predict the future movements of their neighbors to avoid collisions.
• To respond timely and precisely to the environment, high efficiency and accuracy are required in
the prediction.
• Conventional approaches, e.g., LSTM-based models, take considerable computation costs in the
prediction, especially for the long sequence prediction.
• To support more efficient and accurate trajectory predictions, a CNN-based spatial-temporal
graph framework GraphTCN, which captures the spatial and temporal interactions in an input-
aware manner.
• The spatial interaction between agents at each time step is captured with an edge graph
attention network (EGAT), and the temporal interaction across time step is modeled with a
modified gated convolutional network.
• In contrast to conventional models, both the spatial and temporal modeling in GraphTCN are
computed within each local time window.
• Therefore, GraphTCN can be executed in parallel for much higher efficiency, and meanwhile with
accuracy comparable to best-performing approaches.
34. GraphTCN: Spatio-Temporal Interaction Modeling
for Human Trajectory Prediction
The overview of GraphTCN, where EGAT captures the
spatial interaction between agents for each time step
and based on the spatial and historical trajectory
embedding, TCN further captures the temporal
interaction across time steps. The decoder module
then produces multiple socially acceptable
trajectories for all the agents simultaneously.
35. GraphTCN: Spatio-Temporal Interaction Modeling
for Human Trajectory Prediction
TCN with a stack of 3 causal convolution layers of kernel size 3. In each
layer, the left padding is adopted based on the kernel size. The input
contains the spatial information captured by preceding modules. The
output of TCN is collected by concatenating all the outputs across time.
37. Recursive Social Behavior Graph for Trajectory
Prediction
• Social interaction is an important topic in trajectory prediction to generate plausible
paths.
• Force based models utilize the distance to compute force, and they will fail when the
interaction is complicated.
• for pooling methods, the distance between two person at a single timestep is used as a
criterion to calculate the strength of the relationship.
• Attention methods also meet the same problem that Euclidean distance are used in their
method to guide the attention mechanism.
• An insight of group-based social interaction model to explore relationships among
pedestrians.
• recursively extract social representations supervised by group-based annotations and
formulate them into a social behavior graph, called Recursive Social Behavior Graph.
• recursive mechanism explores the representation power largely.
• Graph CNN is used to propagate social interaction information in such a graph.
38. Recursive Social Behavior Graph for Trajectory
Prediction
Overview. For individual representation, BiLSTMs are used to encode historical trajectory feature, and CNNs are
used to encode human context feature. For relational social representation, first generate RSBG recursively and
then use GCN to propagate social features. At the decoding stage, social features are concatenated with individual
features which finally decoded by an LSTM based decoder.