Detecting activities which involve a sequence of complex pose and motion changes in unsegmented videos is a challenging task, and common approaches use sequential graphical models to infer the human pose-state in every frame. We propose an alternative model based on detecting the key-poses in a video, where only the temporal positions of a few key-poses are inferred. We also introduce a novel pose summarization algorithm to automatically discover the key-poses of an activity. We learn a detection filter for each key-pose, which along with a bag-of-words root filter are combined in an HCRF model, whose parameters are learned using the latent-SVM optimization. We evaluate the performance of our model for detection on unsegmented videos on four human action datasets, which include challenging crowded scenes with dynamic backgrounds, inter-person occlusions, multi-human interactions and hard-to-detect daily use objects. © 2014 Springer International Publishing.
CITATION STYLE
Banerjee, P., & Nevatia, R. (2014). Pose filter based hidden-CRF models for activity detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8690 LNCS, pp. 711–726). Springer Verlag. https://doi.org/10.1007/978-3-319-10605-2_46
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