A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Tracking Using Foreground-Background Texture Discrimination
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Semi-interactive tracing of persons in real-life surveillance data
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
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Transactions on data hiding and multimedia security V
Track based relevance feedback for tracing persons in surveillance videos
Computer Vision and Image Understanding
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To help an officer to efficiently review many hours of surveillance recordings, we develop a system of automated video analysis. We introduce a multi-target tracking algorithm that operates on recorded video. Apart from being robust to visual challenges (like partial and full occlusion, variation in illumination and camera view), our algorithm is also robust to temporal challenges, i.e., unknown variation in frame rate. The complication with variation in frame rate is that it invalidates motion estimation. As such, tracking algorithms that are based on motion models will show decreased performance. On the other hand, appearance based tracking suffers from a plethora of false detections. Our tracking algorithm, albeit relying on appearance based detection, deals robustly with the caveats of both approaches. The solution rests on the fact that we can make fully informed choices; not only based on preceding, but also based on following frames. It works as follows. We assume an object detection algorithm that is able to detect all target objects that are present in each frame. From this we build a graph structure. The detections form the graph's nodes. The vertices are formed by connecting each detection in one frame to all detections in the following frame. Thus, each path through the graph shows some particular selection of successive object detections. Object tracking is then reformulated as a heuristic search for optimal paths, where optimal means to find all detections belonging to a single object and excluding any other detection. We show that this approach, without an explicit motion model, is robust to both the visual and temporal challenges.