WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

Zhang, Shifeng; Xie, Yiliang; Wan, Jun; Xia, Hansheng; Li, Stan Z.; Guo, Guodong

doi:10.1109/tmm.2019.2929005

Cited by 125 publications

(68 citation statements)

References 61 publications

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“…To further verify the effectiveness of our algorithm network, our algorithm was tested on another pedestrian detection dataset Caltech. Based on the experimental results obtained on the Caltech dataset, we compared the proposed algorithm with mainstream algorithms, such as RPN + BF [2], Faster R-CNN [4], GDFL [32], SDS-RCNN [35], AMS-Net [30], MGAN [12], ALFNet [29], HyperLearner [36], WiderPerson [31], DR-CNN [28], CSP [3], Couple [37] and Mask-SSD [38], is shown in Fig. 11.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

Xia

Wan

et al. 2021

Appl Intell

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With pedestrian detection algorithms, balancing the trade-off between accuracy and speed remains challenging. Following the central point-based one-stage object detection paradigm, a pedestrian detection algorithm based on multi-scale attention feature aggregation (MAFA) is proposed to improve accuracy while considering real-time performance. We refer to the proposed algorithm as MAFA-Net. Through the design of deep dilate blocks, deeper features are extracted. Pedestrian attention blocks are added to mine more relevant information between features from the perspective of spatial and passage-wise dimensions, and pedestrian features are enhanced. Feature aggregation modules are used to fuse different scale features, and combine the rich high-level semantic features with the accurate location features of the low-level features. Experiments were conducted on two challenging pedestrian detection datasets, i.e., CityPersons and Caltech, using MR −2 as the evaluation index. For Caltech, MR −2 is 4.58% under reasonable conditions. For CityPersons, MR −2 is 11.47% and 10.05% under reasonable and partial occlusion conditions, which is 0.43% and 1.35% better than the suboptimal comparison detection method. The results demonstrate that a good performance is obtained, and the effectiveness and feasibility of the algorithm are verified.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

Xia

Wan

et al. 2021

Appl Intell

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“…The statistics for different water surface environments are shown in Figure 3. There are 1,771 images of ocean environments, 4,114 images of lake environments, and 1,581 images of river environments, respectively accounting for 24, 55, and 21% of WSODD (Zhang et al, 2020). It should be noted that the ship category in WSODD covers only seas and rivers, because Xuanwu Lake and Nanhaizi Lake are small lakes that cannot host large ships.…”

Section: Dataset Statisticsmentioning

confidence: 99%

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

Zhou

Sun

et al. 2021

Front. Neurorobot.

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Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level. To accelerate the development of water surface autonomous driving, this paper proposes a large-scale, high-quality annotated benchmark dataset, named Water Surface Object Detection Dataset (WSODD), to benchmark different water surface object detection algorithms. The proposed dataset consists of 7,467 water surface images in different water environments, climate conditions, and shooting times. In addition, the dataset comprises a total of 14 common object categories and 21,911 instances. Simultaneously, more specific scenarios are focused on in WSODD. In order to find a straightforward architecture to provide good performance on WSODD, a new object detector, named CRB-Net, is proposed to serve as a baseline. In experiments, CRB-Net was compared with 16 state-of-the-art object detection methods and outperformed all of them in terms of detection precision. In this paper, we further discuss the effect of the dataset diversity (e.g., instance size, lighting conditions), training set size, and dataset details (e.g., method of categorization). Cross-dataset validation shows that WSODD significantly outperforms other relevant datasets and that the adaptability of CRB-Net is excellent.

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“…Behind those different methods, there are several datasets (Dalal and Triggs 2005;Overett et al 2008;Silberstein et al 2014;Zhang et al 2019b) that provide strong support for pedestrian detection in the last decade. The Tsinghua-Daimler Cyclist (TDC) (Li et al 2016) dataset focuses on cyclists recorded from a vehicle-mounted stereo vision camera, containing a large number of cyclists varying widely in appearance, pose, scale, occlusion and viewpoint.…”

Section: Related Workmentioning

confidence: 99%

PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

Chi

Zhang

Xing

et al. 2020

AAAI

Self Cite

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Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling ability to existing region-based detection networks without bringing extra computations in the inference stage. Specifically, we design a mask-guided module to leverage the head information to enhance the feature representation learning of the backbone network. Moreover, we develop a strict classification criterion by improving the quality of positive samples during training to eliminate common false positives of pedestrian detection in crowded scenes. Besides, we present an occlusion-simulated data augmentation to enrich the pattern and quantity of occlusion samples to improve the occlusion robustness. As a consequent, we achieve state-of-the-art results on three pedestrian detection datasets including CityPersons, Caltech-USA and CrowdHuman. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. The proposed dataset, source codes and trained models are available at https://github.com/ChiCheng123/PedHunter.

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WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

Cited by 125 publications

References 61 publications

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

MAFA-net: pedestrian detection network based on multi-scale attention feature aggregation

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection

PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

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