ADAPTIVE EDGE-ENHANCED CORRELATION BASED ROBUST AND REAL-TIME VISUAL TRACKING FRAMEWORK AND ITS DEPLOYMENT IN MACHINE VISION SYSTEMS

Abstract

An adaptive edge-enhanced correlation based robust and real-time visual tracking framework, and two machine vision systems based on the framework are proposed. The visual tracking algorithm can track any object of interest in a video acquired from a stationary or moving camera. It can handle the real-world problems, such as noise, clutter, occlusion, uneven illumination, varying appearance, orientation, scale, and velocity of the maneuvering object, and object fading and obscuration in low contrast video at various zoom levels. The proposed machine vision systems are an active camera tracking system and a vision based system for a UGV (unmanned ground vehicle) to handle a road intersection. The core of the proposed visual tracking framework is an Edge Enhanced Back-propagation neural-network Controlled Fast Normalized Correlation (EE- BCFNC), which makes the object localization stage efficient and robust to noise, object fading, obscuration, and uneven illumination. The incorrect template initialization and template-drift problems of the traditional correlation tracker are handled by a best-match rectangle adjustment algorithm. The varying appearance of the object and the short-term neighboring clutter are addressed by a robust template- updating scheme. The background clutter and varying velocity of the object are handled by looking for the object only in a dynamically resizable search window, in which the likelihood of the presence of the object is high. The search window is created using the prediction and the prediction error of a Kalman filter. The effect of the long-term neighboring clutter is reduced by weighting the template pixels using a 2D Gaussian weighting window with adaptive standard deviation parameters. The occlusion is addressed by a data association technique. The varying scale of the object is handled by correlating the search window with three scales of the template, and accepting the best-match region that produces the highest peak in the three correlation surfaces. The proposed visual tracking algorithm is compared with the traditional correlation tracker and, in some cases, with the mean-shift and the condensation trackers on real-world imagery. The proposed algorithm outperforms them in robustness and executes at the speed of 25 to 75 frames/second depending on the current sizes of the adaptive template and the dynamic search window. The proposed active camera tracking system can be used to get the target always in focus (i.e. in the center of the video frame) regardless of the motion of the target in the scene. It feeds the target coordinates estimated by the visual tracking framework into a predictive open-loop car-following control (POL-CFC) algorithm which in turn generates the precise control signals for the pan-tilt motion of the camera. The performance analysis of the system shows that its percent overshoot, rise time, and maximum steady state error are 0%, 1.7 second, and ±1 pixel, respectively. The hardware of the proposed vision based system, that enables a UGV to handle a road intersection, consists of three on-board computers and three cameras (mounted on top of the UGV) looking towards the other three roads merging at the intersection. The software in each computer consists of a vehicle detector, the proposed tracker, and a finite state machine model (FSM) of the traffic. The information from the three FSMs is combined to make an autonomous decision whether it is safe for the UGV to cross the intersection or not. The results of the actual UGV experiments are provided to validate the robustness of the proposed system. Index terms – visual tracking, adaptive edge-enhanced correlation, active camera, unmanned ground vehicle.