Action Detection in Complex Scenes with Spatial and Temporal Ambiguities

In this paper, we investigate the detection of semantic human actions in complex scenes. Unlike conventional action recognition in well-controlled environments, action detection in complex scenes suffers from cluttered backgrounds, heavy crowds, occluded bodies, and spatialtemporal boundary ambiguities caused by imperfect human detection and tracking. Conventional algorithms are likely to fail with such spatial-temporal ambiguities. In this work, the candidate regions of an action are treated as a bag of instances. Then a novel multiple-instance learning framework, named SMILE-SVM (Simulated annealingMultiple Instance LEarning Support Vector Machines), is presented for learning human action detector based on imprecise action locations. SMILE-SVM is extensively evaluated with satisfactory performances on two tasks: 1) human action detection on a public video action database with cluttered backgrounds, and 2) a real world problem of detecting whether the customers in a s...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Yuxiao Hu, Liangliang Cao, Fengjun Lv, Shuicheng Yan
• Keywords: Action Detection, Computer Vision, ICCV 2009