Human action recognition is a rapidly developing field in computer vision. Accurate algorithmic modeling of action recognition must contend with a multitude of challenges. Machine vision and pattern recognition algorithms can be used to aid in the identification of these actions. In recent years research has focused on recognizing complex actions using simple features. Simple cases of action recognition, wherein one individual is captured performing a single action, form the foundation for developing more complex scenarios in real environments. This can be especially useful for surveillance of public locations such as subways, shopping centers, or parking lots in order to reduce crime, monitor traffic flow, and offer security in general. An effective action recognition algorithm must address the following challenges that affect feature extraction for accurate representation : non-rigidity, spatial-variance, temporal-variance, camera perspective. Where face detection seeks to identify the location of an individual's face, activity recognition seeks to recognize the motion or action of an individual. There is generally a commonality of features in the true positive set with face recognition; certain rigid features are present on every human face. Action recognition, on the other hand, must deal with the non-rigidity of the human body. The arms and legs can be at a number of positions relative to one another, and at varying distances and angles. These relative positions describe actions or intermediary poses.
We consider developing a taxonomic shape driven algorithm to solve the problem of human action recognition and develop a new feature extraction technique using hull convexity defects. To test and validate this approach, we use silhouettes of subjects performing ten actions from a commonly used video database by action recognition researchers. A morphological algorithm is used to filter noise from the silhouette. A convex hull is then created around the silhouette frame, from which convex defects will be used as the features for analysis. A complete feature consists of thirty individual values which represent the five largest convex hull defects areas. A consecutive sequence of these features form a complete action. Action frame sequences are preprocessed to separate the data into two sets based on perspective planes and bilateral symmetry. Features are then normalized to create a final set of action sequences. We then formulate and investigate three methods to classify ten actions from the database. Testing and training of the nine test subjects is performed using a leave one out methodology. Classification utilizes both PCA and minimally encoded neural networks. Performance evaluation results show that the Hull Convexity Defect Algorithm provides comparable results with less computational complexity. This research can lead to a real time performance application that can be incorporated to include distinguishing more complex actions and multiple person interaction.