A Multi-Feature Fusion Based Pedestrian Detection Method

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.

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A Vehicle and Pedestrian Detection Method Based on Improved YOLOv4-Tiny

International Journal of Science and Engineering Applications ◽

10.7753/ijsea1101.1003 ◽

2022 ◽

Vol 11 (01) ◽

pp. 22-26

Author(s):

Hui Xiang ◽

Junyan Han ◽

Hanqing Wang ◽

Hao Li ◽

Shangqing Li ◽

...

Keyword(s):

Detection Method ◽

Feature Fusion ◽

Pedestrian Detection ◽

Mean Average Precision ◽

Detection Methods ◽

Small Scale ◽

Detection Accuracy ◽

Improved Method ◽

Average Precision ◽

The Mean

Aiming at the problems of low detection accuracy and poor recognition effect of small-scale targets in traditional vehicle and pedestrian detection methods, a vehicle and pedestrian detection method based on improved YOLOv4-Tiny is proposed. On the basis of YOLOv4-Tiny, the 8-fold down sampling feature layer was added for feature fusion, the PANet structure was used to perform bidirectional fusion for the deep and shallow features from the output feature layer of backbone network, and the detection head for small targets was added. The results show that the mean average precision of the improved method has reached 85.93%, and the detection performance is similar to that of YOLOv4. Compared with the YOLOv4-Tiny, the mean average precision of the improved method is increased by 24.45%, and the detection speed reaches 67.83FPS, which means that the detection effect is significantly improved and can meet the real-time requirements.

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A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes

Sensors ◽

10.3390/s21041240 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1240

Author(s):

Yang Liu ◽

Hailong Su ◽

Cao Zeng ◽

Xiaoli Li

Keyword(s):

Detection Method ◽

Selection Process ◽

Pedestrian Detection ◽

Thermal Infrared ◽

Single Shot ◽

Detection Algorithms ◽

Complex Scenes ◽

Thermal Infrared Images ◽

Online Feature Selection ◽

Huge Challenge

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).

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Research on Multi-feature and Machine Learning Hierarchical Pedestrian Detection Method Based on Deep Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/1748/2/022001 ◽

2021 ◽

Vol 1748 ◽

pp. 022001

Author(s):

Yitong Hao

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Detection Method ◽

Pedestrian Detection

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Object detection method based on dense connection and feature fusion

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) ◽

10.1109/icmcce51767.2020.00381 ◽

2020 ◽

Author(s):

Jiang LiJia ◽

Jiang JiaFu

Keyword(s):

Object Detection ◽

Detection Method ◽

Feature Fusion

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Random Forest with Adaptive Local Template for Pedestrian Detection

Mathematical Problems in Engineering ◽

10.1155/2015/767423 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Tao Xiang ◽

Tao Li ◽

Mao Ye ◽

Zijian Liu

Keyword(s):

Computer Vision ◽

Random Forest ◽

Classification Accuracy ◽

Template Matching ◽

Detection Method ◽

State Of The Art ◽

Pedestrian Detection ◽

Sliding Window ◽

Experimental Results ◽

Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

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A Novel Fast Pedestrian Detection Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.738-739.538 ◽

2015 ◽

Vol 738-739 ◽

pp. 538-541

Author(s):

Fu Qiang Zhou ◽

Yan Li

Keyword(s):

Video Streaming ◽

Local Binary Pattern ◽

Detection Method ◽

Pedestrian Detection ◽

Original Method ◽

Histograms Of Oriented Gradients ◽

Detection Approach

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

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Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.542-543.937 ◽

2012 ◽

Vol 542-543 ◽

pp. 937-940

Author(s):

Ping Shu Ge ◽

Guo Kai Xu ◽

Xiu Chun Zhao ◽

Peng Song ◽

Lie Guo

Keyword(s):

Traditional Method ◽

Detection Method ◽

Feature Vector ◽

Pedestrian Detection ◽

Region Of Interest ◽

Experimental Results ◽

Process Time ◽

Vertical Edge ◽

Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

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