scholarly journals Fast Pedestrian Detection in Surveillance Video Based on Soft Target Training of Shallow Random Forest

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12415-12426 ◽  
Author(s):  
Sangjun Kim ◽  
Sooyeong Kwak ◽  
Byoung Chul Ko
Keyword(s):  
Random Forest ◽  
Pedestrian Detection ◽  
Surveillance Video ◽  
Soft Target

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
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.

Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection

2015 ◽  
Author(s):  
Alejandro Gonzalez ◽  
Gabriel Villalonga ◽  
Jiaolong Xu ◽  
David Vazquez ◽  
Jaume Amores ◽  
...  
Keyword(s):  
Random Forest ◽  
Pedestrian Detection ◽  
Lidar Data

scholarly journals Video Pedestrian Detection Based on Orthogonal Scene Motion Pattern

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Jianming Qu ◽  
Zhijing Liu ◽  
Wenhua He
Keyword(s):  
Prior Knowledge ◽  
Pedestrian Detection ◽  
Motion Pattern ◽  
Surveillance Video ◽  
New Approach ◽  
Negative Image ◽  
Motion Patterns ◽  
Negative Images ◽  
Detection Window ◽  
Set Up

In fixed video scenes, scene motion patterns can be a very useful prior knowledge for pedestrian detection which is still a challenge at present. A new approach of cascade pedestrian detection using an orthogonal scene motion pattern model in a general density video is developed in this paper. To statistically model the pedestrian motion pattern, a probability grid overlaying the whole scene is set up to partition the scene into paths and holding areas. Features extracted from different pattern areas are classified by a group of specific strategies. Instead of using a unitary classifier, the employed classifier is composed of two directional subclassifiers trained, respectively, with different samples which are selected by two orthogonal directions. Considering that the negative images from the detection window scanning are much more than the positive ones, the cascade AdaBoost technique is adopted by the subclassifiers to reduce the negative image computations. The proposed approach is proved effectively by static classification experiments and surveillance video experiments.

scholarly journals Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meiman Li ◽  
Wenfu Xie
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Motion Path ◽  
Support Vector ◽  
Foreground Detection ◽  
Surveillance Video ◽  
Detection Range ◽  
Window Method ◽  
Fusion Detection

For the surveillance video images captured by monocular camera, this paper proposes a method combining foreground detection and deep learning to detect moving pedestrians, making full use of the invariable background of video image. Firstly, the motion region is extracted by the method of interframe difference and background difference. Then, the normalized motion region extracts the feature vectors based on the improved YOLOv3 tiny network. Finally, the trained linear support vector machine is used for pedestrian detection, and the performance of the fusion detection algorithm on caviar dataset is given, which proves the effectiveness of the proposed fusion detection algorithm. Experimental results show that the proposed method not only improves the practical application of pedestrian rerecognition but also reduces the detection range, computational complexity, and false detection rate compared with sliding window method.

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