Bilateral counting network for single-image object counting

He, Lei; Zhang, Shihui; Kong, Weihang

doi:10.1007/s00371-019-01769-5

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…We compare the proposed Count‐DANet with several counting methods and it achieves a significant improvement on different items in the GAME metric, and the detailed results are shown in Table 1. It can be deduced that the Count‐DANet has a 29.4% improvement of GAME(0) over Zhang et al [15] on this dataset and also outperforms other methods, except for the method proposed in [19]. Compared to the method proposed in [19], the proposed method achieves an inferior result than it and the value of the proposed method GAME(0) is only lower by 0.1 point, showing that the proposed method has a competitive performance with the method proposed in [19].…”

Section: Methodsmentioning

confidence: 80%

“…We compare our method with several typical crowd counting methods on Shanghaitech_A dataset: Marsden et al [29], Zhang et al [12], Sindagi and Patel [18], Sam et al [13], Li et al [16], SaCNN [14], Li et al [19], and Zhang et al [15], the results of which are shown in Table 3. As is shown in Table 3, the proposed method gets a 0.3% improvement of MAE with Li et al [19] and achieves the lowest 85.9 of MAE among them, except the method proposed in [15]. Compared to the method proposed in [15], the result of the proposed method on Shanghaitech_A is lower than it by 5.9% and the proposed method achieves higher performance on TRANCOS by 29.4%.…”

Section: Methodsmentioning

confidence: 99%

“…Sindagi and Patel [18] proposed a cascaded CNN to add the context cues from a classification model to the counting CNN. Li et al [19] proposed a bilateral object counting network for extracting different‐level context for counting tasks. To extract multi‐scale features and make the feature with the pixel‐level attention, we design an atrous pyramid attention (APA) module based on the pyramid structure and spatial attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Object counting method based on dual attention network

Zhang

Kong

2020

IET Image Processing

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Section: Methodsmentioning

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Object counting method based on dual attention network

Zhang

Kong

2020

IET Image Processing

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“…The traffic scenario of this dataset is highly complex, and the scale of the counting object varies widely. The experimental comparison shows that our method is superior to the other state-of-the-art methods (Li et al, , 2020(Li et al, , 2023Fu et al, 2023).…”

Section: Comparisons With State-of-the-artmentioning

confidence: 87%

Counting dense object of multiple types based on feature enhancement

Fu,

Min,

Sheng

et al. 2024

Front. Neurorobot.

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IntroductionAccurately counting the number of dense objects in an image, such as pedestrians or vehicles, is a challenging and practical task. The existing density map regression methods based on CNN are mainly used to count a class of dense objects in a single scene. However, in complex traffic scenes, objects such as vehicles and pedestrians usually exist at the same time, and multiple classes of dense objects need to be counted simultaneously.MethodsTo solve the above issues, we propose a new multiple types of dense object counting method based on feature enhancement, which can enhance the features of dense counting objects in complex traffic scenes to realize the classification and regression counting of dense vehicles and people. The counting model consists of the regression subnet and the classification subnet. The regression subnet is primarily used to generate two-channel predicted density maps, mainly including the initial feature layer and the feature enhancement layer, in which the feature enhancement layer can enhance the classification features and regression counting features of dense objects in complex traffic scenes. The classification subnet mainly supervises classifying dense vehicles and people into two feature channels to assist the regression counting task of the regression subnets.ResultsOur method is compared on VisDrone+ datasets, ApolloScape+ datasets, and UAVDT+ datasets. The experimental results show that the method counts two kinds of dense objects simultaneously and outputs a high-quality two-channel predicted density map. The counting performance is better than the state-of-the-art counting network in dense people and vehicle counting.DiscussionIn future work, we will further improve the feature extraction ability of the model in complex traffic scenes to classify and count a variety of dense objects such as cars, pedestrians, and non-motor vehicles.

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“…The mall dataset is similar to UCSD, which contains single scene and a small number of pedestrians. On this dataset, this work gets the best result compared with [7, 8, 42, 43, 44]. It shows that MSR‐FAN has good generalization ability for general datasets.…”

Section: Methodsmentioning

confidence: 99%

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Zhao

Min

Wei

et al. 2021

IET Image Processing

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Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. The existing methods typically using local features cannot properly handle the perspective distortion and the varying scales in congested scene images, and henceforth perform wrong people counting. To alleviate this issue, this study proposes a multi-scale residual feature-aware network (MSR-FAN) that combines multi-scale features using multiple receptive field sizes and learns the featureaware information on each image. The MSR-FAN is trained end-to-end to generate highquality density map and evaluate the crowd number. The method consists of three parts. To handle the perspective changes problem, the first part, the direction-based featureenhanced network, is designed to encode the perspective information in four directions based on the initial image feature. The second part, the proposed multi-scale residual block module, gets the global information to handle the represent the regional feature better. This module explores features of different scales as well as reinforce the global feature. The third part, the feature-aware block, is designed to extract the feature hidden in the different channels. Experiment results based on benchmark datasets show that the proposed approach outperforms the existing state-of-the-art methods.

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Bilateral counting network for single-image object counting

Cited by 9 publications

References 24 publications

Object counting method based on dual attention network

Object counting method based on dual attention network

Counting dense object of multiple types based on feature enhancement

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

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