Object tracking based on Kalman filter and gait feature extraction

Abstract

Real time object tracking is an essential part of surveillance and robot applications. The performance of any object tracking system depends on its accuracy and its ability to deal with various sizes of objects, for better results the tracking should happen at a high speed. Object tracking can be defined as the process of segmenting the region of interest from the video sequence and keeping track of the motion in order to extract useful information. The basic fundamentals of video surveillance such as object detection, tracking and recognition using multiple cameras. Whenever an object enters a video frame, it is detected using background subtraction and the detected objects are tracked with a novel Bayesian Kalman filter with simplified Gaussian mixture (BKF-SGM). Object recognition algorithm is applied using Gait method which is employed to support multiple object tracking with its robust performance. Processing of image is done in MATLAB to get detection, tracking and recognized results.