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Detecting Anomalous Behavior with Surveillance​ Analytics​

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Surveillance feed has essentially been monitored manually until recent years. Video analytics as a technology has made great strides and leverages video surveillance networks to derive searchable, actionable, and quantifiable intelligence from live or recorded video content.

Driven by artificial intelligence and deep learning, video intelligence solutions detect and extract objects in a video. These solutions identify target objects based on trained Deep Neural Networks and then classify each object to enable intelligent video analysis, including search & filtering, alerting, data aggregation and visualization.

In our session, we will:

Discuss the current state of surveillance and popular Python libraries used in video analytics
Elucidate various approaches deployed, using a myriad of pre-trained models from MobileNet SSD to the state-of-the-art Yolo Model.
Describe the many pre-processing techniques we have used, such as the generation of a time-averaged frame, erosion, dilation, and many others
With the basics covered, it’s LIGHTS! CAMERA! ACTION ….Let us show you how this works. We will be presenting a live demo that will explain the performance-computing trade-offs between the use of different models, techniques, and their limitations.

What you can expect to take away from our session:

Gain a deeper understanding of advanced Video Analytics techniques
Understand how to utilize pre-trained models for video analytics solutions
Learn more about the hardware requirements, limitations and challenges posed while devising a video analytics solution
Benefit from the lessons learnt upon deployment in a real-life scenario
The future direction and possibilities of the solution we have developed

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Detecting Anomalous Behavior with Surveillance​ Analytics​

  1. 1. Detecting Anomalous Behavior with Surveillance Analytics Automating security protocols & detecting potential crimes
  2. 2. Speakers § Vinamre Dhar, Data Scientist (vinamre.dhar@kmati.in) § Rishan Sanjay, Data Scientist (rishan.sanjay@kmati.in)
  3. 3. Agenda • Popular analysis utilizing surveillance data • Challenges in existing surveillance solutions • Our proposed solution and demonstration • Architecting for scale
  4. 4. Popular use-cases for Surveillance Analytics ▪ Physical role-based access management ▪ Violent behavior detection in public spaces ▪ Crime and intrusion alerts • Flag and log activity: create searchable events • Trend forecasting • Covid protocols in public spaces • Fire and other safety protocols Monitoring Security • License plate recognition • Infrastructure bottlenecks and unused spaces • Automation and customer intelligence solutions Miscellaneous
  5. 5. Challenges in traditional surveillance Error prone, Slow & not Scalable • Manual monitoring is error-prone, slow, expensive to scale and often an unfeasible endeavor • Human monitoring introduces individual biases • Limited integration with security alerting tools like burglar alarms
  6. 6. Use cases tackled To detect and trigger anomalies without manual intervention • Abandoned object detection • Loitering and unauthorized individual detection • Unauthorized vehicle entry or location detection
  7. 7. Object Detection Ingest Store Cleansing and Pre- Processing Video Data Video Feed (unstructured) Real Time Streaming Protocol Architecture Diagram • Background Subtraction • Erosion • Dilation • Time Avg Frame Source 1. Abandoned object 2. Loitering Individuals 3. Unauthorized vehicle Activity log and incident video updated to Central Monitoring System
  8. 8. Accessing the live video feed Communication protocol used • Real Time Streaming Protocol (RTSP) : communicating with DVR • The VideoCapture() object gathers live video from device using RTSP • A general format of the RTSP protocol looks like: rtsp://username:password@IP:port_no/streaming/channels/camera_no/
  9. 9. Preprocessing and cleaning of the video feed • Live video feed require a low latency fluent frame rate • VideoCapture object is set to skip frame(s) if they aren't received within threshold, to avoid overloading the protocol thread • Gray scaling conversion is important to obtain a binary format(scaled) frame • Pre-processing steps: § Gray scaling § Gaussian blur § Background subtraction § Erosion § Dilation
  10. 10. Video Processing Key modules • Background subtraction: Extrapolate foreground objects from the background • Erosion and dilation: obtaining object contours clearly • Time averaged frame: accurately extract foreground objects, accounts for changing lighting conditions, transient objects in frame and noise • Object recognition: OpenCV for simple shaped objects like boxes • Pretrained model on the COCO dataset for complex objects like people • Threshold prediction scores are used to reduce false positive detections
  11. 11. Metrics • 25 – 30 FPS on a GPU enabled system • Business Impact: • Video feed summarized by activity log of searchable events • Cost reduction for scalable deployment • Using AI significantly increases accuracy and improves responsiveness
  12. 12. Key considerations to note • Compute: For real time analytics, GPU based computing will be critical • Network latency: Its presence may cause a delay in real-time analytics • Databricks Delta facilitates efficient probing of summarized video data • Object detection and tracking models becomes significantly easy using MLflow experiment management
  13. 13. Serve Ingest Store Prep and Train Data Lake Storage Activity Logs (unstructured) Data Factory MLFlow Experiment Management Video Feed (unstructured) Sensors and IoT (unstructured) Scaling with Databricks Delta Format Raw Format 1. Abandoned object 2. Loitering Individuals 3. Unauthorized vehicle Activity log and video summary updated to Central Monitoring System
  14. 14. Conclusions Demonstrated video analytics on a live surveillance feed Open-source packages and frameworks used: OpenCV, Numpy, Tensorflow and Keras Developed a real time surveillance analytics pipeline
  15. 15. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions. Reach out to us on sales@kmati.in

Surveillance feed has essentially been monitored manually until recent years. Video analytics as a technology has made great strides and leverages video surveillance networks to derive searchable, actionable, and quantifiable intelligence from live or recorded video content. Driven by artificial intelligence and deep learning, video intelligence solutions detect and extract objects in a video. These solutions identify target objects based on trained Deep Neural Networks and then classify each object to enable intelligent video analysis, including search & filtering, alerting, data aggregation and visualization. In our session, we will: Discuss the current state of surveillance and popular Python libraries used in video analytics Elucidate various approaches deployed, using a myriad of pre-trained models from MobileNet SSD to the state-of-the-art Yolo Model. Describe the many pre-processing techniques we have used, such as the generation of a time-averaged frame, erosion, dilation, and many others With the basics covered, it’s LIGHTS! CAMERA! ACTION ….Let us show you how this works. We will be presenting a live demo that will explain the performance-computing trade-offs between the use of different models, techniques, and their limitations. What you can expect to take away from our session: Gain a deeper understanding of advanced Video Analytics techniques Understand how to utilize pre-trained models for video analytics solutions Learn more about the hardware requirements, limitations and challenges posed while devising a video analytics solution Benefit from the lessons learnt upon deployment in a real-life scenario The future direction and possibilities of the solution we have developed

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