Name
Abstract | ||
|---|---|---|
In this paper, we propose a mathematical framework to jointly model related activities with both motion and context information for activity recognition and anomaly detection. This is motivated from observations that activities related in space and time rarely occur independently and can serve as context for each other. The spatial and temporal distribution of different activities provides useful cues for the understanding of these activities. We denote the activities occurring with high frequencies in the database as normal activities. Given training data which contains labeled normal activities, our model aims to automatically capture frequent motion and context patterns for each activity class, as well as each pair of classes, from sets of predefined patterns during the learning process. Then, the learned model is used to generate globally optimum labels for activities in the testing videos. We show how to learn the model parameters via an unconstrained convex optimization problem and how to predict the correct labels for a testing instance consisting of multiple activities. The learned model and generated labels are used to detect anomalies whose motion and context patterns deviate from the learned patterns. We show promising results on the VIRAT Ground Dataset that demonstrates the benefit of joint modeling and recognition of activities in a wide-area scene and the effectiveness of the proposed method in anomaly detection. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/JSTSP.2012.2234722 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
database,context pattern,video signal processing,spatial distribution,generated labels,learned model,motion information,context information,wide-area scene,convex programming,anomaly detection,context-aware activity recognition,motion pattern,structural model,temporal distribution,context-aware anomaly detection,learning process,virat ground dataset,training data,unconstrained convex optimization problem,image motion analysis | Training set,Computer vision,Anomaly detection,Activity recognition,Pattern recognition,Computer science,Artificial intelligence,Convex optimization,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 1 | 1932-4553 |
Citations | PageRank | References |
16 | 0.59 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingying Zhu | 1 | 410 | 26.41 |
| Nandita M. Nayak | 2 | 78 | 4.68 |
| Amit K. Roy Chowdhury | 3 | 1153 | 73.96 |