Vehicle re-identification in tunnel scenes via synergistically cascade forests

Zhu, Rixing; Fang, Jianwu; Li, Shuying; Wang, Qi; Xu, Hongke; Xue, Jianru; Yu, Hongkai

doi:10.1016/j.neucom.2019.11.069

Cited by 14 publications

(11 citation statements)

References 26 publications

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“…They extracted features based on Harris corner detection and OpponentSIFT descriptors, considering color information [ 16 ]. Zhu et al [ 5 ] proposed a synergistically cascaded forest model to gradually construct the linking relationships between vehicle samples with increasing alternative random forest and extremely randomized forest layers.…”

Section: Related Workmentioning

confidence: 99%

“…Existing methods mainly perform research on vehicle ReID based on the vehicle appearance [ 4 ]. However, due to the special and complex tunnel environment containing dim illumination and limited viewing field, it is more challenging for the tunnel vehicle ReID problem than that in open road scenes [ 5 , 6 ]. Thus, large fluctuation can be seen by merely conducting tunnel vehicle ReID based on the appearance information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Jia

Wang

et al. 2023

Computational Intelligence and Neuroscience

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Vehicles transporting hazardous material (HAZMAT) pose a severe threat to highway safety, especially in road tunnels. Vehicle reidentification is essential for identifying and warning abnormal states of HAZMAT vehicles in road tunnels. However, there is still no public dataset for benchmarking this task. To this end, this work releases a real-world tunnel HAZMAT vehicle reidentification dataset, VisInt-THV-ReID, including 10,048 images with 865 HAZMAT vehicles and their spatiotemporal information. A method based on multimodal information fusion is proposed to realize vehicle reidentification by fusing vehicle appearance and spatiotemporal information. We design a spatiotemporal similarity determination method for vehicles based on the spatiotemporal law of vehicles in tunnels. Compared with other reidentification methods based on multimodal information fusion, i.e., PROVID, Visual + ST, and Siamese-CNN, experimental results show that our approach significantly improves the vehicle reidentification recognition precision.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Jia

Wang

et al. 2023

Computational Intelligence and Neuroscience

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show abstract

“…The above attention model has been proven to be an effective method to improve the performance of deep neural networks. Inspired by these works, more scholars [28][29][30][31][32][33][34][35] now use an attention mechanism in person Re-ID tasks. Chen et al [31] propose two different attention branches in person Re-ID tasks to enable the learned feature map to perceive persons and related body parts, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The above attention model has been proven to be an effective method to improve the performance of deep neural networks. Inspired by these works, more scholars [28–35] now use an attention mechanism in person Re‐ID tasks. Chen et al.…”

Section: Related Workmentioning

confidence: 99%

Part‐level attention networks for cross‐domain person re‐identification

Qun

Du²,

Ouyang³

et al. 2021

IET Image Processing

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Person re-identification (Re-ID) is in significant demand for intelligent security and single or multiple-target tracking. However, there are issues in the person Re-ID tasks, such as sharp decline in cross-data sets detection accuracy, poor generalization and cross-domain ability of the model. This work mainly studies the generalization and adaptation of crossdomain person Re-ID models. Different from most existing methods for cross-domain Re-ID tasks, the authors use diversified spatial semantic feature in pixel-level learning in the target domain to improve the generality and adaptability of the model. In the case that no information of the target domain is used during the model training, the trained model is directly tested on the data set of the target domain. It has proven effective to add the attention cascade module into the backbone network combining with the part-level branch. The authors conducted extensive experiments based on the three data sets of Market-1501, DukeMTMC-ReID and MSMT17, resulting in both single-domain and cross-domain tests with an average improvement of Rank1 and mAP values of about 10% compared with Baseline through the authors' proposed method named Part-Level Attention Network.

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“…Recently, gcForest has been applied in the classification of schizophrenia data [21], disease classification [22] and cancer detection [23,24,25]. gcForest has not only achieved great success in the medical field, but has also been applied in other domains such as remote sensing [26,27], facial age estimation [28] and EEG processing [29].…”

Section: Introductionmentioning

confidence: 99%

DBC-Forest: Deep forest with binning confidence screening

Ma¹,

Wu²,

Li³

et al. 2021

Preprint

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As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications. Compared with the traditional deep forest approach, gcForestcs effectively reduces the high time cost by passing some instances in the high-confidence region directly to the final stage. However, there is a group of instances with low accuracy in the high-confidence region, which are called mis-partitioned instances. To find these mis-partitioned instances, this paper proposes a deep binning confidence screening forest (DBC-Forest) model, which packs all instances into bins based on their confidences. In this way, more accurate instances can be passed to the final stage, and the performance is improved. Experimental results show that DBC-Forest achieves highly accurate predictions for the same hyperparameters and is faster than other similar models to achieve the same accuracy.

show abstract

Vehicle re-identification in tunnel scenes via synergistically cascade forests

Cited by 14 publications

References 26 publications

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Part‐level attention networks for cross‐domain person re‐identification

DBC-Forest: Deep forest with binning confidence screening

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