This paper presents a technique for view invariant human detection and extending this idea to recognize basic human actions like walking, jogging, hand waving and boxing etc. To achieve this goal we detect the human in its body parts and then learn the changes of those body parts for action recognition. . A complex human activity is modeled as a sequence of elementary human actions like walking, running jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping we extend this approach to recognize actions based on component-wise Hidden Markov Models (HMM). This is achieved by designing a HMM for each action, which is trained based on the detected body parts. Consequently, we are able to distinguish between similar actions by only considering the body parts which has major contributions to those actions e.g. legs for walking, running etc; hands for boxing, waving etc.

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Human body detection Gabor filter bank