Fused Discriminative Metric Learning for Low Resolution Pedestrian Detection

Li, Xinzhao; Liu, Yuehu; Chen, Zeqi; Zhou, Jiahuan; Wu, Ying

doi:10.1109/icip.2018.8451791

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Chen et al [24] performed multi-stage distillation to learn the light-weight network for acceleration. Li et al [106] transformed the LR feature space into a new LR classification space using an optimal Mahanalobis metric. Xie et al [211] proposed to assign a large weight to the proposal in crowded scene.…”

Section: Pure Cnn Based Pedestrian Detection Methodsmentioning

confidence: 99%

From Handcrafted to Deep Features for Pedestrian Detection: A Survey

Cao

Pang

Xie

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. Here we present a comprehensive survey on recent advances in pedestrian detection. First, we provide a detailed review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches. For handcrafted features based methods, we present an extensive review of approaches and find that handcrafted features with large freedom degrees in shape and space have better performance. In the case of deep features based approaches, we split them into pure CNN based methods and those employing both handcrafted and CNN based features. We give the statistical analysis and tendency of these methods, where feature enhanced, part-aware, and post-processing methods have attracted main attention. In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance. Furthermore, we introduce some related datasets and evaluation metrics, and compare some representative methods. We conclude this survey by emphasizing open problems that need to be addressed and highlighting various future directions. Researchers can track an up-to-date list at https://github.com/JialeCao001/PedSurvey.

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Section: Pure Cnn Based Pedestrian Detection Methodsmentioning

confidence: 99%

From Handcrafted to Deep Features for Pedestrian Detection: A Survey

Cao

Pang

Xie

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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“…In [ 21 ], a multi-resolution generative adversarial network (MRGAN) is proposed to simultaneously conduct multiresolution pedestrian detection, by generating high-resolution pedestrian images from low-resolution images. In [ 22 ], a fused discriminative metric learning (F-DML) approach is proposed to learn the optimal Mahanalobis metric, which transforms the low-resolution feature space into a new classification space, to improve the detection accuracy for low-resolution images. In addition, some works focus on pedestrian detection in low-light conditions.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Pedestrian Detection Engine with Two-Stage Low-Complexity Detection Network and Adaptive Region Focusing Technique

Que

Zhang

Guo

et al. 2021

Sensors

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Pedestrian detection has been widely used in applications such as video surveillance and intelligent robots. Recently, deep learning-based pedestrian detection engines have attracted lots of attention. However, the computational complexity of these engines is high, which makes them unsuitable for hardware- and power-constrained mobile applications, such as drones for surveillance. In this paper, we propose a lightweight pedestrian detection engine with a two-stage low-complexity detection network and adaptive region focusing technique, to reduce the computational complexity in pedestrian detection, while maintaining sufficient detection accuracy. The proposed pedestrian detection engine has significantly reduced the number of parameters (0.73 M) and operations (1.04 B), while achieving a comparable precision (85.18%) and miss rate (25.16%) to many existing designs. Moreover, the proposed engine, together with YOLOv3 and YOLOv3-Tiny, has been implemented on a Xilinx FPGA Zynq7020 for comparison. It is able to achieve 16.3 Fps while consuming 0.59 W, which outperforms the results of YOLOv3 (5.3 Fps, 2.43 W) and YOLOv3-Tiny (12.8 Fps, 0.95 W).

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