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

Li, Zhe; Chen, Yangzhou; Yin, Zhuo

doi:10.1007/s42452-019-0852-2

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…The above results show that the proposed approach is better than the blob detection by 25.4% and Kalman Filter [17] by 20.6%, as per the predicted results.…”

Section: Figure 6 Vehicle Detection Accuracysupporting

confidence: 76%

“…It has been clear from the above results that the proposed approach has lesser occlusion as compare to the Blob detection technique and is reduced by 60.05%. The results are also improved from the Kalman filter-based prediction [17] approach and are improved by 47.3%.…”

Section: Figure 6 Vehicle Detection Accuracymentioning

confidence: 95%

“…The other method has been suggested by Li et al [17] to predict vehicles accurately. The proposed algorithm uses a Kalman filter and fuses its information to segment vehicles under the occlusion state and effectively track vehicles.…”

Section: B Feature and Filter Based Approachesmentioning

confidence: 99%

“…The blob detection method detects 3878 unwanted objects in 7152 frames of different datasets, whereas the proposed scheme performed well and detected only 1550 unwanted objects. The other Kalman Filter based Prediction Approach [17] is also designed to predict the vehicles, but it still faces this challenge, and this approach detects around 1642 unwanted objects. Again, it is higher than the proposed technique.…”

Section: Unwanted Object Detectionmentioning

confidence: 99%

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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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“…The above results show that the proposed approach is better than the blob detection by 25.4% and Kalman Filter [17] by 20.6%, as per the predicted results.…”

Section: Figure 6 Vehicle Detection Accuracysupporting

confidence: 76%

Section: Figure 6 Vehicle Detection Accuracymentioning

confidence: 95%

Section: B Feature and Filter Based Approachesmentioning

confidence: 99%

Section: Unwanted Object Detectionmentioning

confidence: 99%

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PredictV: A Vehicle Prediction Scheme to Circumvent Occluded Frames

et al. 2022

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“…It has been widely used in intelligent transportation system (ITS) for obtaining the state information of the outdoor vehicle. However, outdoor vehicle tracking is facing severe challenges due to the complex and changeable outdoor environments, such as illumination variation, occlusion, cluttered background and so on [1], [2]. These challenges are attributed to dramatic changes of object appearance.…”

Section: Introductionmentioning

confidence: 99%

Classifier Adaptive Fusion: Deep Learning for Robust Outdoor Vehicle Visual Tracking

Xin

Shi

2019

IEEE Access

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Deep auto-encoder (DAE) models have been successfully used in object tracking due to its strong capability of feature representation. However, single deep auto-encoder model would not be robust enough to represent the appearance model of outdoor vehicle for its harsh working environment, such as illumination variation, occlusion, cluttered background and so on. In this paper, a novel multiple-DAE-based tracking approach, that is, classifier adaptive fusion for robust outdoor vehicle visual tracking approach is proposed under particle filter framework. Firstly, two deep auto-encoders are offline trained by grayscale image and gradient image of the raw training images, respectively to obtain the stronger feature representation of gray-scale image and gradient image. Secondly, two classifiers are constructed using the encoder of the two well-trained deep auto-encoders and the output of the each classifier is used to compute the confidence of the corresponding particles. Finally, the confidence output of the two classifiers is fused and applied in online tracking, where, the fusion weight of the each classifier is computed according to the distribution of particles represented by different classifier. Extensive tracking experiments conducted on visual tracking benchmark (VTB) show that the proposed tracking algorithm outperforms 9 popular tracking algorithms in the challenge scenes of outdoor vehicle tracking such as illumination variation, occlusion, cluttered background and scale variation.INDEX TERMS Classifier adaptive fusion (CAF), multiple deep auto-encoder, outdoor vehicle visual tracking, particle filter.

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