Research on Pedestrian Detection Method with Motion and Shape Features

In this Paper, Propose a Pedestrian Detection Method that Based on Adaboost Algorithm and Pedestrian Shape Features Integration. First According to the Collected Pedestrian True, False Sample, Selected the Characteristics of the Extended Class Haar, Adopt Adaboost Algorithm Training Get Pedestrian Classifier to Split the Initial Candidate Region of All Pedestrians in the Image. in this Paper, Propose an Adaptive Threshold Weight Update Method, Significantly Reduced the Number of the Characteristics of Strong Classifier, Optimize the Classifier Structure, Reduce the Complexity of the Algorithm; Meanwhile, the Online Update Detector, Improving the Reliability of the Detector. Pedestrian Leg Have Strong Vertical Edge Symmetry Characteristic so that Extracted the Vertical Edge Detection in the Initial Candidate Region, According to the Symmetry Determine the Vertical Axis of Symmetry, Combined with the Morphological Characteristics of Pedestrians to Determine the Width and Height Characteristics of the Pedestrian, to Determine the Pedestrian Candidate Region, Finally, Put a Further Validation to the Pedestrian Candidate Region.

Download Full-text

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).

Download Full-text

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

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text