Vulnerable pedestrian detection and tracking using deep learning

2018 ◽  
Author(s):  
Hyok Song ◽  
In Kyu Choi ◽  
Min Soo Ko ◽  
Jinwoo Bae ◽  
Sooyoung Kwak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Detection And Tracking

scholarly journals THE STUDY OF ACTIVATION FUNCTIONS IN DEEP LEARNING FOR PEDESTRIAN DETECTION AND TRACKING

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 53-59 ◽  
Author(s):  
M. N. Favorskaya ◽  
V. V. Andreev
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Linear Part ◽  
Activation Function ◽  
Sigmoid Function ◽  
Human Machine Interaction ◽  
Learning Stage ◽  
Detection And Tracking ◽  
Starting Point ◽  
Indoor Scenes

<p><strong>Abstract.</strong> Pedestrian detection and tracking remains a highlight research topic due to its paramount importance in the fields of video surveillance, human-machine interaction, and tracking analysis. At present time, pedestrian detection is still an open problem because of many challenges of image representation in the outdoor and indoor scenes. In recent years, deep learning, in particular Convolutional Neural Networks (CNNs) became the state-of-the-art in terms of accuracy in many computer vision tasks. The unsupervised learning of CNNs is still an open issue. In this paper, we study a matter of feature extraction using a special activation function. Most of CNNs share the same architecture, when each convolutional layer is followed by a nonlinear activation layer. The activation function Rectified Linear Unit (ReLU) is the most widely used as a fast alternative to sigmoid function. We propose a bounded randomized leaky ReLU working in such manner that the angle of linear part with the highest input values is tuned during learning stage, and this linear part can be directed not only upward but also downward using a variable bias for its starting point. The bounded randomized leaky ReLU was tested on Caltech Pedestrian Dataset with promising results.</p>

Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

2018 ◽  
Vol 300 ◽  
pp. 17-33 ◽  
Author(s):  
Antonio Brunetti ◽  
Domenico Buongiorno ◽  
Gianpaolo Francesco Trotta ◽  
Vitoantonio Bevilacqua
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Pedestrian Detection ◽  
Detection And Tracking ◽  
Learning Techniques

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Deep Learning for Pedestrian Detection

2019 ◽  
Vol 9 (8) ◽  
pp. p9212
Author(s):  
Utkarsha Sagar ◽  
Ravi Raja ◽  
Himanshu Shekhar
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection

A Deep Learning Model for Detection and Tracking in High-Throughput Images of Organoid

2021 ◽  
pp. 104490
Author(s):  
Xuesheng Bian ◽  
Gang Li ◽  
Cheng Wang ◽  
Weiquan Liu ◽  
Xiuhong Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Learning Model ◽  
Detection And Tracking ◽  
Deep Learning Model

scholarly journals Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Yingfeng Cai ◽  
Youguo He ◽  
Hai Wang ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Training Methods ◽  
Two Dimensional ◽  
Data Set ◽  
Deep Model ◽  
Unsupervised Training ◽  
Weakly Supervised

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

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