Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 5, Pages: 1-12
K. Anuradha1* and N. Sairam2
1School of Electrical and Electronics Engineering, SASTRA University, Tirumalaisamudram, Thanjavur - 613401, Tamil Nadu, India; [email protected] 2School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur – 613401, Tamil Nadu, India; [email protected]
*Author For Correspondence
K. Anuradha School of Electrical and Electronics Engineering, SASTRA University, Tirumalaisamudram, Thanjavur - 613401, Tamil Nadu, India; [email protected]
The objective of this review article is to study the spatio-temporal approaches for addressing the key issues such as multi-view, cluttering, jitter and occlusion in recognition of human action. Based on high-level action units, a new sparse model was developed for recognition of human action in static background. Relevant to multi-camera view, a negative space approach for identifying actions taken from different viewing angles was proposed. An approach was based on space-time quantities was proposed to acquire the changes of the action instead of camera motion. This space-time based approach has handled both cluttering and camera jitter. In static background, a sparse model presented for recognition of human action acquires the fact that actions from the same class share same units. The presented method was assessed on numerous public data sets. This method has achieved a recognition rate of 95.49% in KTH dataset and 89% in UCF datasets. Based on negative space, a region based method was offered. This approach has managed the issue of long shadows in human action recognition. The approach was assessed by most common datasets and has attained higher precision than contemporary techniques. An approach based on space-time quantities was proposed to manage cluttering. This approach achieves a recognition rate of 93.18% in KTH dataset and 81.5% in UCF dataset. To handle occlusion, a model was presented with spatial and temporal consistency. The algorithm was appraised on an outdoor dataset with background clutter and a standard indoor dataset (HumanEva-I). Results were matched with advanced pose estimation algorithms.
Keywords: Action Recognition, Camera Jitter, Clutter, Multi-view, Occlusion, Segmentation
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