Series Info:Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
Type:Conference Paper
Keywords:Action recognition
,
Human activity recognition
,
Motion trajectory
,
SIFT algorithm
,
Visual surviellance system
,
Action recognition
,
Human activity recognition
,
Motion trajectories
,
SIFT algorithm
,
Visual surviellance system
,
Algorithms
,
Image recognition
,
Intelligent systems
,
Systems analysis
,
Trajectories
Abstract:
In this paper, we propose a rule-based system for semantically understanding and analyzing the motion of the trajectories of the human activity. The proposed system can be used as a preprocessing phase for enhancing the object detection process. Detected trajectories are classified into three categories; normal, semi-normal and abnormal trajectories according to the distances between their adjacent points. Abnormal trajectories are removed from the trajectory space. Semi-normal trajectories are broken into small normal trajectories that are linked later to form a longer normal trajectory. The proposed system does not assume a specific trajectory length and hence is more generic than similar trajectory enhancement approaches. The effectiveness of the proposed approach is demonstrated through several experimental results using known human motion datasets. � 2010 IEEE.
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