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
International Journal of Computer Vision
Detecting and segmenting humans in crowded scenes
Proceedings of the 15th international conference on Multimedia
Automatic background substitution using monocular camera and temporal foreground probability model
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Journal on Image and Video Processing - Video Tracking in Complex Scenes for Surveillance Applications
International Journal of Computer Vision
MSIADU '09 Proceedings of the 1st ACM SIGMM international workshop on Media studies and implementations that help improving access to disabled users
A comprehensive evaluation framework and a comparative study for human detectors
IEEE Transactions on Intelligent Transportation Systems
Object detection combining recognition and segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Learning to Recognize Objects in Images Using Anisotropic Nonparametric Kernels
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
People detection using color and depth images
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Arbitrary-Shape object localization using adaptive image grids
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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We propose a closely coupled object detection and segmentation algorithm for enhancing both processes in a cooperative and iterative manner. Figure-ground segmentation reduces the effect of background clutter on template matching; the matched template provides shape constraints on segmentation. More precisely, we estimate the probability of each pixel belonging to the foreground by a weighted sum of the estimates based on shape and color alone. The weight on the shape-based estimate is related to the probability that a familiar object is present and is updated dynamically so that we enforce shape constraints only where the object is present. Experiments on detecting people in images of cluttered scenes demonstrate that the proposed algorithm improves both segmentation and detection. More accurate object boundaries are extracted; higher object detection rates and lower false alarm rates are achieved than performing the two processes separately or sequentially.