ACTIVITY RECOGNITION USING HISTOGRAM OF ORIENTED GRADIENT PATTERN HISTORY

Human activity recognition is an important task in computer vision because it has many application areas such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to automatically recognize human activity from input video stream using Histogram of Oriented Gradient Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We have experimented with video data of human activity in real environments for three different tasks (browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system is applicable to recognize human activity in real-life.