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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. Human-body part detection in different views is an extremely challenging problem due to drastic change of 3D-pose of human body, self occlusions etc while performing actions. In order to cope with these problems we have designed three example-based detectors that are trained to find separately three components of the human body, namely the head, legs and arms. We incorporate 10 sub-classifiers for the head, arms and the leg detection. Each sub-classifier detects body parts under a specific range of viewpoints. Then, view-invariance is fulfilled by combining the results of these sub classifiers. Subsequently, 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.