Human activity recognition in low quality videos using spatio-temporal features
Human activity recognition (HAR) is one of the most intensively studied areas of computer vision in recent times. However, under real world conditions, especially when public infrastructure such as surveillance and web cameras are considered, current HAR techniques do not adapt to lower quality vide...
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| Format: | Thesis |
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2016
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| Summary: | Human activity recognition (HAR) is one of the most intensively studied areas of computer vision in recent times. However, under real world conditions, especially when public infrastructure such as surveillance and web cameras are considered, current HAR techniques do not adapt to lower quality videos due to various challenges such as noise and lighting changes, motion blur, poor resolution and sampling. The objective of this research is to develop a framework and methods for human activity recognition using spatio-temporal information from low quality video. Overall, it can be observed that texture is an important visual feature cue for low quality video, and the robustness of shape and motion feature can be strengthened by using this. |
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