This paper presents a multitarget tracking algorithm based on a particle filter framework that exploits a sparse distributed shape model to handle partial occlusions. The state vector is composed by a set of points of interest (i.e. corners) and it enables to jointly describe position and shape of the target. An efficient importance sampling strategy is developed to limit the number of used particles and it is based on multiple Kanade-Lucas-Tomasi (KLT) feature trackers used to estimate local motion. The importance sampling strategy adaptively handles KLT failures and partial occlusions. Particles weights are computed exploiting a shape matching technique combined with object local appearance encoded in color histograms of patches centered on the points of interest constituting the state. The proposed approach does not require background subtraction techniques and overcomes several common difficulties in the tracking domain as partial occlusions, object deformations, scale changes, abrupt motion and non-static background. Extensive experimental results are provided on challenging sequences to demonstrate the robustness of the algorithm.
Abstract.In the last few years, the application of ICT technologies in automotive field has taken an increasing role in improving both the safety and the driving comfort. In this context, systems capable of determining the traffic situation and/or driver behavior through the analysis of signals from multiple sensors (e.g. radar, cameras, etc...) are the subject of active research in both industrial and academic sectors. The extraction of contextual information through the analysis of video streams captured by cameras can therefore have implications in many applications focused both on prevention of incidents and on provision of useful information to drivers. In this paper, we investigate the study and implementation of algorithms for the extraction of context data from on-board cameras mounted on vehicles. A camera is oriented so as to frame the portion of road in front of the vehicle while the other one is positioned inside the vehicle and pointed on the driver.
In the near future automatic systems able to detect the traffic situation and to understand driver behavior and intent will probably become vehicles tool to improve driver safety. Therefore, robust video processing techniques able to cope with difficult environmental road condition such as luminosity changes, dynamic and cluttered background, etc. are necessary for these applications. In this work, lanes detection, vehicle position and traffic analysis are the information extracted to characterize the driving situation and the proposed techniques try to cope with the above mentioned issues. The presented framework is tested using an on-board camera in real-world scenario respecting the real-time constraint and showing good performances in highways and urban roads.
This paper presents a tracking algorithm based on a Sequential Importance Sampling (SIS) Particle Filter scheme followed by a resampling strategy where shape and color cues are exploited to handle deformable objects. The state vector is composed by a set of corners and it enables to jointly describe position and shape of the target. Mean Shift trackers, applied to color cues associated to state subspaces, are employed to predict the target global motion. An adaptive system noise is defined based on this information to cope with local deformations. The updating procedure is accomplished by a shape matching technique. Experimental results prove the effectiveness of the proposed approach with respect to simple deformations, partial occlusions and moving camera.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.