Pose Filter Based Hidden-CRF Models for Activity Detection

Abstract

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.