A Particle-Filtering Approach for Vehicular Tracking Adaptive to Occlusions

Scharcanski, Jacob; Oliveira, Alessandro Bof de; Cavalcanti, Pablo G.; Yari, Yessenia

doi:10.1109/tvt.2010.2099676

Cited by 57 publications

(40 citation statements)

References 16 publications

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“…We compared our RDHOGPF method with an APF [16], an annealing PF [24], a two-stage hybrid tracker (TSHT) [25], and a tracker that fused the color and contour features [28]. These approaches all deliver good performance according to the literature.…”

Section: Resultsmentioning

confidence: 99%

“…In [15], the contextual confidence of the measurement model was also considered, and nonoverlapping fragments were selected dynamically for likelihood measurements. Two operation modes have been proposed for the adaptive particle filter (APF) [16]. When the tracked vehicle is not occluded, APF uses a normal probability density function to generate a new set of particles.…”

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confidence: 99%

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A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking

Kao

Jen

et al. 2014

IEEE Trans. Ind. Electron.

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Abstract-This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an extension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multiobject tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.Index Terms-Histogram of oriented gradients (HOG), particle filter, vehicle tracking.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking

Kao

Jen

et al. 2014

IEEE Trans. Ind. Electron.

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“…In the CSS subcircuit, the observations of survival targets and newborn targets are processed by n separate distance computation units by (8) and (12) accordingly. The unit number n is fixed as n = P + 2, where P is the maximum number of the targets that appear within the observation region at any time.…”

Section: Hardware Implementationmentioning

confidence: 99%

“…For example, in vehicular ad hoc networks (VANETs), it usually needs to track the states of multiple vehicles on the roads based on clutter-contaminated observations for regulators to predict and manage traffic as well as for drivers to achieve driving-safety and route planning [5]- [8]. Since clutters are usually caused by terrain factors, weather systems, and/or unrelated moving objects, such as birds [9], the number of observations varies at different sensing moments.…”

Section: Introductionmentioning

confidence: 99%

POSE: Design of Hardware-Friendly Particle-Based Observation Selection PHD Filter

Shi

Liu

Hong

et al. 2014

IEEE Trans. Ind. Electron.

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Abstract-Particle probability hypothesis density (PHD) filtering is a promising technology for the multitarget-tracking problem. Traditional particle PHD filter solutions usually have high computational complexity, and the lack of dedicated hardware has seriously limited their usages in real-time industrial applications. The hardware implementation difficulty of the particle PHD filtering in field-programmable gate array (FPGA) platforms lies in that the number of observations for filtering is time varying while the number of parallel processing units in circuit is fixed. To overcome this challenge, we propose a novel particle-based observation selection (POSE) PHD filter algorithm and its hardware implementation in this paper. Specifically, we opportunistically select a fixed number of observations out of a varying number of observations for filtering, where the approximation error is proved to be negligible by adapting the circuit budget to the environment accordingly. To implement the proposed POSE PHD filter, the hardware design issues are addressed in depth. Extensive simulations demonstrate that the POSE PHD filter has a comparable performance with the traditional one while its hardware implementation challenge is overcome. The hardware experiment results of the POSE PHD filter on a Xilinx Virtex-II Pro FPGA platform match the simulation ones well. Furthermore, the execution time of the implemented hardware circuit is evaluated, and the results show that it can achieve a processing rate of 6.892 kHz with a 50-MHz system clock.Index Terms-Field-programmable gate array (FPGA) platform, hardware design, multitarget tracking (MTT), particle probability hypothesis density (PHD) filter, real-time performance.

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“…propose to combine a particle filter with the CamShift algorithm for vehicle tracking using video in order to achieve scale-invariability and account for disturbances such as occlusion and background clutter [24]. Another video-based method that is especially robust to partial occlusion of the target was proposed in [25]. Finally, [26] also uses particle filtering for video-based vehicle tracking where particles are clustered by analyzing the motion coherence in order to form convex shapes of the tracked objects.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering

Hostettler

Djurić

2015

IEEE Trans. Veh. Technol.

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This is a post-print of a paper published in IEEE Transactions on Vehicular Technology. When citing this work, you must always cite the original article:R. Hostettler and P. M. Djurić, "Vehicle tracking based on fusion of magnetometer and accelerometer sensor measurements with particle filtering," Vehicular Technology, IEEE Transactions on, vol. 64,no. 11, Abstract-In this article, we propose a method for vehicle tracking on roadways using measurements of magnetometers and accelerometers. The measurements are used to build a low-cost, low-complexity vehicle tracking sensor platform for highway traffic monitoring. First, the problem is formulated by introducing the process model for the motion of the vehicle on the road and two measurement models, one for each of the sensors. Second, it is shown how the measurements of the sensors can be fused using particle filtering. The standard sampling importance resampling (SIR) particle filter is extended for processing of multi-rate sensor measurements and models that employ unknown static parameters. The latter are treated by Rao-Blackwellization. The performance of the method is demonstrated by computer simulations. It is found that it is feasible to fuse the two sensors for vehicle tracking and that the proposed multi-rate particle filter performs better than particle filters that process only measurements of one of the sensors. The main contribution of this paper is the novel approach of fusing the measurements of road-mounted magnetometers and accelerometers for vehicle tracking and traffic monitoring.

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A Particle-Filtering Approach for Vehicular Tracking Adaptive to Occlusions

Cited by 57 publications

References 16 publications

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking

POSE: Design of Hardware-Friendly Particle-Based Observation Selection PHD Filter

Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering

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