Vehicle tracking under occlusion conditions using directional ringlet intensity feature transform

Krieger, Evan; Sidike, Paheding; Aspiras, Theus; Asari, Vijayan K.

doi:10.1109/naecon.2015.7443041

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…In the literature, target tracking algorithms under occlusion conditions are mainly divided into five categories, [1]: (1) center weighted regions matching [2,3], (2) subblock matching [4][5][6][7][8][9], (3) trajectory prediction [2,4,6,8,10], (4) Bayesian theory [4,10,11], (5) multiple algorithm fusion [2][3][4][5][6][7][8][9][10][11]. Wang et al [2] use the Mean-Shift algorithm for vehicle tracking in the absence of occlusion; when occlusion occurs, the GM(1,1) model based on historical vehicle position information is used to predict the vehicle position at the next moment.…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al [7] propose the RDHOGPF feature to solve the problem of partial occlusion of vehicles. Krieger et al [8] propose the DRIFT feature to solve the problem of partial occlusion of vehicles and for the case of complete occlusion they use kalman filter to predict vehicle position. Qing et al [9] use the taillights to adjust the bounding box so as to accurately locate the position of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle tracking fusing the prior information of Kalman filter under occlusion conditions

Chen

Yin

2019

SN Appl. Sci.

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The occlusion in traffic scenarios have posed great challenges in vehicle tracking. Establishing effective and robust vehicle tracking algorithms under occlusion conditions is a necessary issue for many traffic applications. This paper presents a novel approach of vehicle tracking by fusing the prior information of Kalman filter. The prior information of Kalman filter is used for background update, precise morphological operation and occlusion judgment. The fusion of observation and prior information is adopted to segment vehicles under occlusion. It effectively improves the robustness of the vehicle tracking algorithm in the case of occlusion. A novel vehicle description method is proposed to express vehicle shape more accurately and thus reduces the misjudgement of occlusion. Experiments show that the proposed algorithm can accurately track vehicles even under occlusion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle tracking fusing the prior information of Kalman filter under occlusion conditions

Chen

Yin

2019

SN Appl. Sci.

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“…As a result, despite the issue of complexity, its performance was deemed adequate. To predict vehicles, the other Kalman filter-based approach was proposed by Krieger et al [18], and in that method, the first frame, the center point, and the radius of the reference object are selected. For calculating the object's centre in the following frame, the proposed method employs a Kalman filter.…”

Section: B Feature and Filter Based Approachesmentioning

confidence: 99%

PredictV: A Vehicle Prediction Scheme to Circumvent Occluded Frames

et al. 2022

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There are many methods to overcome traffic congestion on the roads, but occlusion is still there in most methods, so it is a dire need of the time that researchers have to look into this matter. In the rural and urban areas, heavy congestion on the roads has become the leading cause of occlusion. The PredictV method works on the prediction principle based on existing values and is proved one of the naval approaches for this problem. This scheme uses blob detection for the first frame detection and predicts other vehicles based on percentage increment in the different parameters to identify the vehicles. For improving the quality of the sample, data cleaning has been included in this work. With the help of this approach, congestion has been avoided, and this method prefers to use predicted points to detect vehicles on the frame. The suggested approach is implemented via a MATLAB simulator. It is tested on a large dataset containing 7152 frames of 6 different videos from the Urban Tracker and KoPer datasets. In total, there are 46876 vehicles present on the frame at first, and the existing methods have detected only 58% and 73% of vehicles, whereas the detection rate is 82% with this suggested approach. The occlusion rate is only 17% on average, which has been reduced via this proposed approach which was 24% and 43% earlier than the existing one. The performance of this suggested method has been tested based on performance and results, which are pretty impressive that is almost 99.74%. In short, the outcome of this prediction technique has been improved now, and the effectiveness has been observed too.

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