Optimization of object tracking based on enhanced imperialist competitive algorithm

Object tracking is one of the most challenging tasks in the field of computer vision. Tracking moving object(s) in video/image frame sequences in cluttered scenes usually results in complications and hence performance degradation. This is attributable to complexity in partial and full object occlusions and scene illumination changes which render object tracking complicated besides the delay in processing of moving images from frame to frame as well as the presence of multiple objects in the video frames under consideration. This paper explores the use of Enhanced Imperialist Competitive Algorithm (EICA) to track moving object(s) in video frames. The results obtained reveal the usefulness of this approach and provide the needed stimulus for further research in the problem domain.Keywords: Imperialist, Optimization, Tracking, Colony, Objec

Embedded Real Time Gesture Tracking

Video tracking is the process of locating a moving object (or several ones) in time using a camera. An algorithm evaluates the video frames and outputs the location of moving targets within the video frame

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

Dynamic Object Tracking by Partial Shape Matching for Video Surveillance Applications

In this thesis, an algorithm for object tracking through frames of video using a fast partial shape matching technique is proposed. The tracking is divided into two modules: 1) moving object extraction followed by color/edge segmentation, and 2) tracking through frames using partial shape matching. The major challenges of object tracking, such as occlusions, splitting of one object and appearance and disappearance of objects, are effectively resolved. The proposed algorithm is tested on several synthetic and real life video sequences and is shown to be very effective in identifying and tracking moving objects independent of translations, rotations, scale variations and occlusions. The novelty of the proposed algorithm lies in its ability to independently track full and partial objects undergoing split, merge and occlusion scenarios independent of their location and scale in the scene. . The technique assumes that: 1) the video frames are captured at 30 frames per second in order for the object(s) motion (translation, rotation, isometric scale variations) to be well modeled by an affine transformation, 2) the object(s) being tracked are larger than a certain number of pixels to allow for comprehensive shape modeling, and 3) the video camera is kept stationary