A Machine Learning Inspired Approach for Detection, Recognition and Tracking of Moving Objects from Real-Time Video

In this paper, we address the problem of recognizing moving objects in video im-ages using Visual Vocabulary model and Bag of Words. Initially, the shadow free images are obtained by background modelling followed by object segmentation from the video frame to extract the blobs of our object of interest. Subsequently, we train a Visual Vocabulary model with human body datasets in accordance with our domain of interest for recognition. In training, we use the principle of Bag of Words to extract necessary features to certain domains and objects for classification, similarly, matching them with extracted object blobs that are obtained by subtracting the shadow free background from the foreground. We track the detected objects via Kalman Filter. We evaluate our algorithm on benchmark datasets. A comparative analysis of our algorithm against the existing state-of-the-art methods shows very satisfactory results to go forward.