Human Activity Recognition Using Multidimensional Indexing

In this paper, we develop a novel method for view-based recognition of human action/activity from videos. By observing just a few frames, we can identify the activity that takes place in a video sequence. The basic idea of our method is that activities can be positively identified from a sparsely sampled sequence of a few body poses acquired from videos. In our approach, an activity is represented by a set of pose and velocity vectors for the major body parts (hands, legs, and torso) and stored in a set of multidimensional hash tables. We develop a theoretical foundation that shows that robust recognition of a sequence of body pose vectors can be achieved by a method of indexing and sequencing and it requires only a few pose vectors (i.e., sampled body poses in video frames). We find that the probability of false alarm drops exponentially with the increased number of sampled body poses. So, matching only a few body poses guarantees high probability for correct recognition. Our approach...

• Type: Journal
• Year: 2002
• Where: PAMI
• Authors: Jezekiel Ben-Arie, Zhiqian Wang, Purvin Pandit, ShyamSundar Rajaram
• Keywords: Hash Tables, Major Body Parts, PAMI 2002, Robust Recognition