Multi-scale Pedestrian Detection Research Articles

YOLOv8-CB: Dense Pedestrian Detection Algorithm Based on In-Vehicle Camera

Recently, the field of vehicle-mounted visual intelligence technology has witnessed a surge of interest in pedestrian detection. Existing algorithms for dense pedestrian detection at intersections face challenges such as high computational weight, complex models that are difficult to deploy, and suboptimal detection accuracy for small targets and highly occluded pedestrians. To address these issues, this paper proposes an improved lightweight multi-scale pedestrian detection algorithm, YOLOv8-CB. The algorithm introduces a lightweight cascade fusion network, CFNet (cascade fusion network), and a CBAM attention module to improve the characterization of multi-scale feature semantics and location information, and it superimposes a bidirectional weighted feature fusion path BIFPN structure to fuse more effective features and improve pedestrian detection performance. It is experimentally verified that compared with the YOLOv8n algorithm, the accuracy of the improved model is increased by 2.4%, the number of model parameters is reduced by 6.45%, and the computational load is reduced by 6.74%. The inference time for a single image is 10.8 ms. The cascade fusion algorithm YOLOv8-CB has higher detection accuracy and is a lighter model for multi-scale pedestrian detection in complex scenes such as streets or intersections. This proposed algorithm presents a valuable approach for device-side pedestrian detection with limited computational resources.

A Novel Efficient Dual-Gate Mixed Dilated Convolution Network for Multi-Scale Pedestrian Detection

With the increasing use of onboard high-speed computing systems, vehicle manufacturers are offering significant advanced features of driver assistance systems. Pedestrian detection is one of the major requirements of such systems, which commonly use cameras, radar, and ultrasonic sensors. Image recognition based on captured image streams is one of the powerful tools used for the detection of pedestrians, which exhibits similarities and distinguishing features compared to general object detection. Although pedestrian detection has advanced significantly along with deep learning, some issues still need to be addressed. Pedestrian detection is essential for several real-world applications and is an initial step in outdoor scene analysis. Typically, in a crowded situation, conventional detectors are unable to distinguish persons from each other successfully. This study presents a novel technique, based on the Dual Gate Mixed Dilated Convolution Network, to address this problem by adaptively filtering spatial areas where the patterns are still complicated and require further processing. The proposed technique manages obscured patterns while offering improved multiscale pedestrian recognition accuracy.

Occlusion and multi-scale pedestrian detection A review

Occlusion and multi-scale pedestrian detection A review

From macro to micro: rethinking multi-scale pedestrian detection

From macro to micro: rethinking multi-scale pedestrian detection

MGA-YOLOv4: a multi-scale pedestrian detection method based on mask-guided attention

MGA-YOLOv4: a multi-scale pedestrian detection method based on mask-guided attention

Occlusion Handling and Multi-Scale Pedestrian Detection Based on Deep Learning: A Review

Pedestrian detection is an important branch of computer vision, and has important applications in the fields of autonomous driving, artificial intelligence and video surveillance. With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and has achieved better performance. However, the performance of state-of-the-art methods is far behind expectations, especially when occlusion and scale variance exist. Therefore, many works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. First, a brief progress of pedestrian detection in the past two decades is summarized. Second, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trends in pedestrian detection are discussed.

A New Multi-scale Pedestrian Detection Algorithm in Traffic Environment

Pedestrian detection in traffic environment requires high accuracy and speed of the algorithm. Traditional methods can meet the speed requirement, but there is a long gap in accuracy. Traditional methods based on convolution neural network have higher accuracy, but the amount of calculation is enormous. Aiming at the problems of background confusion, there are pedestrian ambiguity and pedestrian multi-scale in pedestrian detection, this paper constructs a pedestrian detection method with higher accuracy and faster speed based on Faster Regions Convolutional Neural Network (Faster RCNN) method, which is the leading method in the field of target detection. We have mainly improved three aspects: (1) The design criteria based on the summary and the scale characteristics of pedestrians; (2) The setting of anchor windows and the way of creating regional networks have been adjusted, and the pooling layer of environmental regions has been added; (3) Fusion of different levels of features acquires more comprehensive features. Then, based on the open-source deep learning framework, the network is implemented on the California Institute of Technology pedestrian data set. The experimental results show that our method improves the detection accuracy by 2.9% and the detection speed by 10.1 frames/second compared with the original Faster RCNN on the same data set.

Conditional random fields as message passing mechanism in anchor-free network for multi-scale pedestrian detection

Many deep learning detectors have been proposed to address the scale variation issue in pedestrian detection. However, it is still far from being fully solved because most of these detectors handle the issue by directly fusing multiple extracted features with the weighted average or concatenation. Therefore, this paper designs a novel and effective anchor-free network (namely MPAF-Net) for multi-scale pedestrian detection. Specifically, we first improve Res2Net as our backbone network by designing a novel bottleneck block to make the proposed detector has a strong multi-scale representation ability for pedestrians. Furthermore, based on Conditional Random Fields (CRFs), the multi-scale features obtained from three different feature pyramid levels of backbone network are refined with a designed message passing mechanism. By doing so, the complementary information from the features at other scales can be dynamically passed to enhance the scale-specific feature. Finally, pedestrian detection is simplified as a straightforward center and scale prediction task by performing convolution on the concatenation of refined multi-scale convolution features. This way, the proposed MPAF-Net can be trained in an end-to-end fashion. Experimental results show that MPAF-Net achieves state-of-the-art performance on CityPersons, Caltech, and CrowdHuman benchmarks.

Trans-scale feature aggregation network for multiscale pedestrian detection

Trans-scale feature aggregation network for multiscale pedestrian detection

Scale-Aware Hierarchical Detection Network for Pedestrian Detection

Several or even dozens of times spatial scale variation is one of the major bottleneck for pedestrian detection. Although the Region-based Convolutional Neural Network (R-CNN) families have shown promising results for object detection, they are still limited to detect pedestrians with large scale variations due to the fixed receptive field sizes on a single convolutional output layer. In contrast to previous methods that simply combined pedestrian predictions on feature maps with different resolution, we propose a scale-aware hierarchical detection network for pedestrian detection under large scale variations. First, we introduce a cross-scale features aggregation module to accomplish feature augmentation for pedestrian representation through merging the lateral connection, the top-down path and bottom-up path. Specifically, the cross-scale features aggregation module can adaptively fuse hierarchical features to enhance feature pyramid representation for robust semantic and accurate localization. Further, we design a scale-aware hierarchical detection network to effectively integrate multiscale pedestrian detection into a unified framework by adaptively perceiving the augmented feature level for special-scale pedestrian detection. Experimentally, the proposed scale-aware hierarchical detection network forms a more robust and discriminative model for pedestrian instances with different scales on widely-used ETH and Caltech benchmarks. In particular, compared with the state-of-the-art method FasterRCNN+ATT, the log-average miss rate of pedestrian detection is reduced by 11.98% for medium scale pedestrians (between 30-80 pixels in height), and 14.12% for whole scale pedestrians (above 20 pixels in height) on Caltech benchmark.

Target Detection Algorithm Based on Multi-structure Combination HOG

In order to detect pedestrian targets of different sizes in complex environments, a pedestrian detection method based on multi-structure combination HOG (Histograms of oriented gradients) is proposed. This method generates HOG feature in series through blocks composed of cell of different structures. For new high-dimensional HOG feature, the low-dimensional HOG descriptor is extracted by PCA (Principal Component Analysis) dimensional reduction method. At last, the detection of pedestrian target is realized by using SVM (Support Vector Machine) classification tool. The experiment verified the high accuracy of multi-structure combined HOG in multi-scale pedestrian detection through the INRIADATA.

Multi-scale Pedestrian Detection in Thermal Imaging Using Deep Convolutional Neural Network and Adaptive NMS

Multi-scale Pedestrian Detection in Thermal Imaging Using Deep Convolutional Neural Network and Adaptive NMS

Multi-scale pedestrian detection using skip pooling and recurrent convolution

Detecting pedestrians of different scales is essential for applications like autonomous driving. Recent research progress showed that combining multiple feature maps and contextual information helps detecting objects of different scales. In this paper, we propose a multi-scale pedestrian detector that combines skip pooling from multi-resolution feature maps and recurrent convolutional layers for extracting contextual information. To fully exploit the unique characteristics of the features at different levels for multi-scale pedestrian detection, the multi-scale features and the context features are fused at the fully connected layer. To gather spatial contextual information, we propose a modified recurrent convolutional layer that produces context feature maps with different resolutions. In addition, we construct a set of scale-dependent classification and bounding box regression subnetworks to further improve the performance of multi-scale pedestrian detection. Experiments on Caltech and KITTI pedestrian detection benchmark datasets show that the proposed method achieves the state-of-the-art performance with faster speed.

基于改进全卷积网络的多尺度感知行人检测算法

基于改进全卷积网络的多尺度感知行人检测算法

FPGA Implementation of Multi-scale Pedestrian Detection in Thermal Images

Abstract In this paper an embedded vision system for human silhouette detection in thermal images is presented. As the computing platform a reprogrammable device (FPGA – Field Programmable Gate Array) is used. The detection algorithm is based on a sliding window approach, which content is compared with a probabilistic template. Moreover, detection is four scales in supported. On the used test database, the proposed method obtained 97% accuracy, with average one false detection per frame. Due to the used parallelization and pipelining real-time processing for 720 × 480 @ 50 fps and 1280 × 720 @ 50 fps video streams was achieved. The system has been practically verified in a test setup with a thermal camera.