Track-to-track association for intelligent vehicles by preserving local track geometry

Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation–maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.The authors gratefully acknowledge the Autonomous Vision Group for providing the KITTI dataset. The authors also would like to thank the editors and referees for the valuable comments and suggestions.The Research Funds of Chongqing Science and Technology Commission, the National Natural Science Foundation of China, the Key Project of Crossing and Emerging Area of CQUPT, the Research Fund of young-backbone university teacher in Chongqing province, Chongqing Overseas Scholars Innovation Program, Wenfeng Talents of Chongqing University of Posts and Telecommunications, Innovation Team Project of Chongqing Education Committee, the National Key Research and Development Program, the Research and Innovation of Chongqing Postgraduate Project, the Lilong Innovation and Entrepreneurship Fund of Chongqing University of Posts and Telecommunications.http://www.mdpi.com/journal/sensorsam2021Electrical, Electronic and Computer Engineerin

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD)