A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Vehicle and Person Tracking in Aerial Videos
Multimodal Technologies for Perception of Humans
Segmentation and Recognition Using Structure from Motion Point Clouds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Tracking multiple objects in non-stationary video
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Adaptive multiple object tracking using colour and segmentation cues
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multi-object tracking in non-stationary video using bacterial foraging swarms
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detection and tracking of large number of targets in wide area surveillance
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Monocular online learning for road region labeling and object detection from a moving platform
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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We present a novel approach to detect and track independently moving regions in a 3D scene observed by a moving camera in the presence of strong parallax. Detected moving pixels are classified into independently moving regions or parallax regions by analyzing two geometric constraints: the commonly used epipolar constraint, and the structure consistency constraint. The second constraint is implemented within a "Plane+Parallax" framework and represented by a bilinear relationship which relates the image points to their relative depths. This newly derived relationship is related to trilinear tensor, but can be enforced into more than three frames. It does not assume a constant reference plane in the scene and therefore eliminates the need for manual selection of reference plane. Then, a robust parallax filtering scheme is proposed to accumulate the geometric constraint errors within a sliding window and estimate a likelihood map for pixel classification. The likelihood map is integrated into our tracking framework based on the spatio-temporal Joint Probability Data Association Filter (JPDAF). This tracking approach infers the trajectory and bounding box of the moving objects by searching the optimal path with maximum joint probability within a fixed size of buffer. We demonstrate the performance of the proposed approach on real video sequences where parallax effects are significant.