DESIGN AND IMPLEMENTATION OF AN OBJECT TRACKING SYSTEM CONTROL USING PID AND MOVEMENT PREDICTION

The tracking system usually has some lack of problem, that is unstable system when the object moved so the tracking process can’t define the object position well. On the other hands, when the object moves, the system can’t track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control was used to improve the stability of tracking system, so that the result became more stable than before, it can be seen from errorof tracking. Otherwise, to looking for again the undetected object, a linier regression method was used in purpose to get more faster in finding the new position of a movement object that was disappear from the views of camera before . When the object on unmoved condition, the system has error value ±15 pixel. For horizontal move condition of servo on slow motion ±9.4 pixel, also on the fast motion, the error values is about ±20.1 pixel. For the servo with vertical movement ,the error value is about ±13.4 pixel for the slow motion and ±45.7 pixel for fast motion. The process on finding the object that was disappear from the views of camera before, ±2 second. Finally it can be concluded that the add of PID control and linear regression method, make the tracking system become more stabile and real time. Keyword : tracking object, PID control, regression method, real time system

UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

CoMaL Tracking: Tracking Points at the Object Boundaries

Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched. This is inefficient and may also lose many points that are not re-detected in the next frame. We propose a novel tracking algorithm to accurately and efficiently track CoMaL points. For this, the level line segment associated with the CoMaL points is matched to MSER segments in the next frame using shape-based matching and the matches are further filtered using texture-based matching. Experiments show improvements over a simple re-detect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on Tracking and Surveillance, CVPR 201