Detection and Localization of Road Area in Traffic Video Sequences Using Motion Information and Fuzzy-Shadowed Sets

Human motion self-occlusion due to motion overlapping in the same region is a daunting task to solve. Various motion-recognition methods either bypass this problem or solve this problem in complex manner. Appearance-based template matching paradigms are simpler and hence faster approaches for activity analysis. In this paper, we concentrate on motion self-occlusion problem due to motion overlapping in various complex activities for recognition. This paper illustrates the directional motion history image concept and compares this motion representation approach with multi-level motion history representation and hierarchical motion history histogram representation to solve the self-occlusion problem of basic motion history image representation. We employ some complex aerobics and find the robustness of our method compared to other methods for this self-occlusion problem. We employ seven higher order Hu moments to compute the feature vector for each activity. Afterwards, k-nearest neighbor method is utilized for classification with leave-one-out paradigm. The comparative results clearly demonstrate the superiority of our method than other recent approaches.

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Robust Human Motion Recognition Based on Multi-directional Motion Representation

Ahad

Ogata

Tan

et al. 2007

Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007)

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Detection and Localization of Road Area in Traffic Video Sequences Using Motion Information and Fuzzy-Shadowed Sets

Cited by 6 publications

References 8 publications

Comparative analysis between two view-based methods: MHI and DMHI

Comparative analysis between two view-based methods: MHI and DMHI

Solutions to motion self-occlusion problem in human activity analysis

Robust Human Motion Recognition Based on Multi-directional Motion Representation

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