[Partly in Japanese]
I present it in 関東コンピュータビジョン勉強会(2015/07/25).
Main References:
http://crcv.ucf.edu/projects/GMMCP-Tracker/CVPR15_GMMCP_Presentation.pptx
If you find a problem, please let me know.
Thanks!
Efficient and Effective Influence Maximization in Social Networks: Hybrid App...NAVER Engineering
발표자: 고윤용(한양대 박사과정)
발표일: 2018.2.
Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximize their influence spread over a social network. Kempe et al. showed the problem to be NP-hard and proposed a greedy algorithm (referred to as SimpleGreedy) that guarantees 63% influence spread of its optimal solution. However, SimpleGreedy has two performance issues: at a micro level, it estimates the influence spread of a single node by running Monte-Carlo (MC) simulations that are fairly expensive; at a macro level, after selecting one seed at each step, it re-evaluates the influence spread of every node in a social network, leading to significant computational overhead. In this paper, we propose Hybrid-IM that addresses the two issues in both micro and macro levels by combining PB-IM (Path Based Influence Maximization) and CB-IM (Community Based Influence Maximization). Furthermore, we identify two technical issues that could improve the performance of Hybrid-IM more and propose two strategies to address those issues. Through extensive experiments with four real-world datasets, we show that Hybrid-IM achieves great improvement (up to 43 times) in performance over state-of-the-art methods and finds the seed set that provides the influence spread very close to that of the state-of-the-art methods.
https://telecombcn-dl.github.io/2018-dlcv/
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Abstract – For various application detection of objects movement in a video is an important process. Determination of path of object as time advances is a tedious step. Many proposal for tracking the multiple movement of object has been put forward using various sophisticated techniques. In this paper detail description of the recent object trackers based on particle filtering and Markov Models have been analyzed. The outcome of the analysis is computational efficiency, robustness and computational complexity.
Computer m
emory is expensive and the recording of data captured by a webcam needs memory. I
n order to minimize the
memory usage in recording data from human motion as recorded from the webcam, this algorithm will use motion
detection as applied to a process to measure the change in speed or vector of an object in the field of view. This
applicat
ion only works if there is a motion detected and it will automatically save the captured image in its designated
folder.
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This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Efficient and Effective Influence Maximization in Social Networks: Hybrid App...NAVER Engineering
발표자: 고윤용(한양대 박사과정)
발표일: 2018.2.
Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximize their influence spread over a social network. Kempe et al. showed the problem to be NP-hard and proposed a greedy algorithm (referred to as SimpleGreedy) that guarantees 63% influence spread of its optimal solution. However, SimpleGreedy has two performance issues: at a micro level, it estimates the influence spread of a single node by running Monte-Carlo (MC) simulations that are fairly expensive; at a macro level, after selecting one seed at each step, it re-evaluates the influence spread of every node in a social network, leading to significant computational overhead. In this paper, we propose Hybrid-IM that addresses the two issues in both micro and macro levels by combining PB-IM (Path Based Influence Maximization) and CB-IM (Community Based Influence Maximization). Furthermore, we identify two technical issues that could improve the performance of Hybrid-IM more and propose two strategies to address those issues. Through extensive experiments with four real-world datasets, we show that Hybrid-IM achieves great improvement (up to 43 times) in performance over state-of-the-art methods and finds the seed set that provides the influence spread very close to that of the state-of-the-art methods.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Multi Object Tracking Methods Based on Particle Filter and HMMIJTET Journal
Abstract – For various application detection of objects movement in a video is an important process. Determination of path of object as time advances is a tedious step. Many proposal for tracking the multiple movement of object has been put forward using various sophisticated techniques. In this paper detail description of the recent object trackers based on particle filtering and Markov Models have been analyzed. The outcome of the analysis is computational efficiency, robustness and computational complexity.
Computer m
emory is expensive and the recording of data captured by a webcam needs memory. I
n order to minimize the
memory usage in recording data from human motion as recorded from the webcam, this algorithm will use motion
detection as applied to a process to measure the change in speed or vector of an object in the field of view. This
applicat
ion only works if there is a motion detected and it will automatically save the captured image in its designated
folder.
Meta Dropout: Learning to Perturb Latent Features for Generalization MLAI2
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test examples, we may achieve better generalization performance. However, obtaining such perturbation is not possible in standard machine learning frameworks as the distribution of the test data is unknown. To tackle this challenge, we propose a novel regularization method, meta-dropout, which learns to perturb the latent features of training examples for generalization in a meta-learning framework. Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner. Then, the learned noise generator can perturb the training examples of unseen tasks at the meta-test time for improved generalization. We validate our method on few-shot classification datasets, whose results show that it significantly improves the generalization performance of the base model, and largely outperforms existing regularization methods such as information bottleneck, manifold mixup, and information dropout.
Presented at JavaOne 2017 [CON4027], this presentation takes a practical, hands-on look at Java performance tuning. It discusses methodology (spoiler: it’s the scientific method) and how to apply it to Java SE systems (on any budget). Exploring concrete examples with tools such as the Oracle Java Mission Control feature of Oracle Java SE Advanced, VisualVM, YourKit, and JMH, the presentation focuses on ways of measuring performance, how to interpret data, ways of eliminating bottlenecks, and even how to avoid future performance regressions.
A separate version will be uploaded with speaker notes.
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In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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https://arxiv.org/abs/2306.08302
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Defect reporting
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[Paper introduction] GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
1. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
2015 / 7 / 26 (Fri.)
関東コンピュータビジョン勉強会
発表者: @hokkun_cv
GMMCP-Tracker:
Globally Optimal Generalized Maximum
Multi Clique Problem for Multiple Object Tracking
1
Afshin Dehghan, Shayan Modiri Assari, Mubarak Shah
University of Central Florida
2. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
About me
• 東大院・学際情報学府・M2
• 相澤研究室所属
• 普段は食べものの研究をしています
• 2014/5のCV勉強会(CNNについて)ぶりの発表
参加です
2
• Preferred Networksでインターン→アルバイト中
• メンターが@tabe2314さん
• 今日はその課題の中で出てきたタスクに関連する
論文を紹介します
3. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
対象とする問題
• Multiple Object Tracking (MOT)
• YouTubeデモ (GMMCP)
3
※筆者は物体追跡については専門ではないので細かいとこ
ろに誤りがある可能性があります.遠慮無く指摘をお願い
致します.
4. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
ちなみに
• 筆者らはMultiple Object Trackingにかかわる論文
をもうひとつCVPR2015で発表している(強い)
• Target Identity-aware Network Flow for Online Multiple
Target Tracking
• 筆頭著者も一緒(Ph.Dの学生,ちなみに去年も2本筆頭で発表.強い)
4
5. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
その他
• H. Possegger et al., In Defense of Color-based
Model-free Tracking
• モデルフリートラッキング(非detection based)
• T. Liu et al., Real-time part-based visual tracking
via adaptive correlation filters
• パートベースのトラッキング
• S. Tang et al., Subgraph Decomposition for Multi-
Target Tracking
5
6. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data Association (Naïvest)
6
Frame n Frame n+1
Bipartite
Matching
Problem
7. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Tracking
7
Detection
Data
Association
http://crcv.ucf.edu/projects/GMMCP-Tracker/CVPR15_GMMCP_Presentation.pptx
8. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Tracking
8
Detection
Data
Association
http://crcv.ucf.edu/projects/GMMCP-Tracker/CVPR15_GMMCP_Presentation.pptx
9. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data Association (Naïvest)
9
Frame n Frame n+1
Bipartite
Matching
Problem
10. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data Association (Naïvest)
10
Frame n Frame n+1
Bipartite
Matching
Problem
11. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data Association (Network Flow)
11
Frame n Frame n+1 Frame n+2 Frame n+3
sources
sinks
minimum-cost
maximum-flow
problem
• incorporating
motion feature
• multi-commodity
network
12. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
12
Frame
1
Frame
2
Frame
3
13. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
However,
• Data association with network flow is simplified
formulation of this problem
• Assuming no simplification is closer to the
tracking scenario in real world.
13
14. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data Association (Not Simplify)
14
Frame n
Frame n+1
Frame n+2
Frame n+3
重み
=
0.95
重み
=
0.10
うまいこと重みが最大
になるクリークを探す
15. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Preliminary: clique (クリーク)
• 任意の2点を結ぶ枝がある頂点集合のこと
• see wikipedia in detail
• 今回は「各クラスタから1つのノードを選んでで
きる部分グラフ」という理解でOK
15
16. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Data Association (Not Simplify)
16
Frame n
Frame n+1
Frame n+2
Frame n+3
Input: k-partite complete
graph (完全k部グラフ)
A person form a clique
↓
maximum clique
problem
17. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
GMCP Tracker[1]
• The same team s ECCV 2012 paper
• They formulate MOT as generalized maximum
clique problem. (cf. former page)
17[1] Amir Roshan Zamir et al., GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs, ECCV, 2012.
18. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
However (2),
• Due to complexity of the model, these
approaches have been solved by approximate
solutions.
• GMCP Tracker also used a greedy local
neighborhood search, which is prone to local
minima.
• GMCP Tracker doesn t follow a joint optimization
for all the tracks simultaneously (one by one).
18
19. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Contribution
1. this approach doesn t involve any simplification
neither in formulation nor in optimization
(Binary Integer Problem).
2. they propose a more efficient occlusion
handling strategy, which can handle long-term
occlusions (e.g. 150 frames) and can speed-up
the whole algorithm.
19
20. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Contribution
1. this approach doesn t involve any simplification
neither in formulation nor in optimization
(Binary Integer Problem).
2. they propose a more efficient occlusion
handling strategy, which can handle long-term
occlusions (e.g. 150 frames) and can speed-up
the whole algorithm.
20
21. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
21
Low-level Tracklets
Segment 01 Segment 05
Segment 06 Segment 10
Mid-level Tracklets
Final Trajectories
GMMCP GMMCP
Input Video
Human
Detection
Detected Humans
22. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
22
Low-level Tracklets
Segment 01 Segment 05
Segment 06 Segment 10
Mid-level Tracklets
Final Trajectories
GMMCP GMMCP
Input Video
Human
Detection
Detected Humans
23. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Step 0: Low-level Tracklet
• In GMCP, the nodes at first step are each
detections.
23
Frames
1-‐10
• In GMMCP, the nodes are (low-level) tracklet
• How to find: bounding boxes that overlap more than
60% between two frames are regarded as being
connected.
24. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
24
Low-level Tracklets
Segment 01 Segment 05
Segment 06 Segment 10
Mid-level Tracklets
Final Trajectories
GMMCP GMMCP
Input Video
Human
Detection
Detected Humans
25. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Step 1: Mid-level Tracklet
25
• 各クラスタ(青円)からひとつのノード(赤線)
を選び,クリークを作る
Frames
1-‐10
Frames
11-‐20
Frames
21-‐30
Frames
31-‐40
Frames
41-‐50
Frames
51-‐60
26. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Step 1: Mid-level Tracklet
26
• エッジの重み = (見た目特徴) + (動き特徴)
• これを基に最適化をすると・・
Frames
1-‐10
Frames
11-‐20
Frames
21-‐30
Frames
31-‐40
Frames
41-‐50
Frames
51-‐60
27. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Step 1: Mid-level Tracklet
27
• このような三人の軌跡が同時に検出できる
• オクルージョンに対応するため,ダミーノードを
入れてある
Frames
1-‐10
Frames
11-‐20
Frames
21-‐30
Frames
31-‐40
Frames
41-‐50
Frames
51-‐60
28. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
28
Low-level Tracklets
Segment 01 Segment 05
Segment 06 Segment 10
Mid-level Tracklets
Final Trajectories
GMMCP GMMCP
Input Video
Human
Detection
Detected Humans
29. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Step 2: Final Trajectories
• The another but similar problem with step 1.
• They solve GMMCP:
• Nodes are Mid-level Tracklet
• For appearance feature, they use median (or average)
feature among detections in each frame
• For motion feature, they use middle point of mid-level
tracklet as the location of each node
29
30. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Appearance Affinity
• Feature: Invariant Color Histogram [2]
• Deformation and viewpoint invariant
• Affinity: Histogram Intersection
30[1] J. Domke et al., Deformation and Viewpoint Invariant Color Histogram, BMVC, 2006
min(H1[i], H2[i])
31. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Motion Affinity
31[1] J. Domke et al., Deformation and Viewpoint Invariant Color Histogram, BMVC, 2006
今の位置
前の位置+速度度から
予想される位置
32. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Optimization
• GMMCP is NP Hard, but they solve without any
simplification.
• They formulate GMMCP as Binary Integer
Problem (BIP, 0-1整数計画問題)
32
33. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
33http://www.dais.is.tohoku.ac.jp/ shioura/teaching/dais08/dais02.pdf
34. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
34http://www.dais.is.tohoku.ac.jp/ shioura/teaching/dais08/dais02.pdf
35. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Optimization
• GMMCP is NP Hard, but they solve without any
simplification.
• They formulate GMMCP as Binary Integer
Problem (BIP, 0-1整数計画問題)
35
• これは実は組合せ最適化と言われる問題
• cf. 0-1ナップザック問題,巡回セールスマン問
題
36. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
BIP in this case
• C is weight matrix (?)
• x is boolean column vector
• the elements of x is all of edges and nodes
• Ax = b is equality constraints
• Mx <= n is inequality constraints
36
37. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
37
各クラスタごとに1に
なってるのは定数K
Notation
: i th node in j th cluster
: edge between and h: Number of clusterseij
mn
vm
n
vi
j
vi
j
あるノードから伸び
るエッジはh-1(かゼ
ロ)
クリークを作ってい
るかどうか
3種の制約
38. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Contribution
1. this approach doesn t involve any simplification
neither in formulation nor in optimization
(Binary Integer Problem).
2. they propose a more efficient occlusion
handling strategy, which can handle long-term
occlusions (e.g. 150 frames) and can speed-up
the whole algorithm.
38
39. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Occlusion Handling
• Detector can detect not all the persons in each
frame
• Occlusion, Detection Error, …
• They add Dummy Node to each cluster
• Cost of dummy edge ( = edge connected to
dummy node) is fixed value.
39
40. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Occlusion Handling
40
= さっきまで出てきてた重み ( 見た目 + 動き )cj1
cj2 = 定数c_d
cj3 , cj4 = 0
41. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Occlusion Handling
• How/How many do we add dummy nodes?
• Many dummy nodes increase computational
complexity
• cf. case of GMCP:
• They add dummy node by the motion-based way
• ある答えに対して等速度運動を仮定して,大きくハズレ
てしまうようなクラスタにダミーノードを足す
• Many dummy nodes increase computational
complexity (大事なので2度)
41
42. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Occlusion Handling
• Aggregated Dummy Nodes (ADN)
• no longer be boolean variable
• can take any integer value
• add only one ADN to each cluster
• Not connected to other nodes!
• New Solution: Mixed-Binary-Integer Programming
42
Constraint 1 Constraint 2 Constraint 3
各クラスタごとに1に
なってるのは定数K
あるクラスタから伸
びるエッジは1か0
クリークを作ってい
るかどうか
43. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Occlusion Handling
43
cj1
cj2 はない (
cj3 , cj4= 0
cd
2
cj3 , cj4 =
= さっきまで出てきてた重み ( 見た目 + 動き )
51.
GMMCP
Tracker:
Globally
Op3mal
Generalized
Maximum
Mul3
Clique
Problem
for
Mul3ple
Object
Tracking
TUD-Stadmitte
Mid-‐level
Tracklets
Final
Trajectories
52.
GMMCP
Tracker:
Globally
Op3mal
Generalized
Maximum
Mul3
Clique
Problem
for
Mul3ple
Object
Tracking
53.
GMMCP
Tracker:
Globally
Op3mal
Generalized
Maximum
Mul3
Clique
Problem
for
Mul3ple
Object
Tracking
54. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
まとめ (拝借)
• Formulate MOT as GMMCP
• a new graph theoretic problem
• Formulate GMMCP as a MBIP
• GMMCP is NP Hard but no approximate solutions
• An efficient occlusion handling through AND
• Performance close to real-time
• Improving state-of-art on several sequences
55. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
project page
• 本物のproject pageが情報量多くてこれがCVPR複
数本2年連続で通す人のページか,と思いました
• http://crcv.ucf.edu/projects/GMMCP-Tracker/
55
56. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking
Implementation Detail
• Detection: DPM
• K: target-specific
• (1st layer ) number of cluster: 5 (2-6で実験)
• (2nd layer) number of cluster: 6
56
Editor's Notes
Data AssociationベースのトラッカーはとてもDetectorの性能に依存するよね
だからどっちも一緒に勉強しようね
この手のはいくつかはある
ベクトルは黒板でsつめい
----- Meeting Notes (5/4/15 14:46) -----
fix the video