scholarly journals Human Detection via Image Denoising for 5G-Enabled Intelligent Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hui Li ◽  
Hang Zhou ◽  
Xiaoguo Liang ◽  
Fen Cai ◽  
Lingwei Xu ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Multiple Scales ◽  
Autonomous Driving ◽  
Detection Performance ◽  
Feature Representation ◽  
Human Detection ◽  
Intelligent Video Surveillance ◽  
Video Images ◽  
Detection Technology ◽  
Efficient Transmission

5G technology strongly supports the development of various intelligent applications, such as intelligent video surveillance and autonomous driving. And the human detection technology in intelligent video surveillance has also ushered in new challenges. A number of video images will be compressed for efficient transmission; the resulting incomplete feature representation of images will drop the human detection performance. Therefore, in this work, we propose a new human detection method based on compressed denoising. We exploit the quality factor in the compressed image and incorporate the pixel_shuffle inverse transform based on FFDNet to effectively improve the performance of image compression denoising, then HRNet and HRFPN are used to extract and fuse high-resolution features of denoised images, respectively, to obtain high-quality feature representation, and finally, a cascaded object detector is used for classification and bounding box regression to further improve object detection performance. At last, the experimental results on PASCAL VOC show that the proposed method effectively removes the compression noise and further detects human objects with multiple scales and different postures. Compared with the state-of-the-art methods, our method achieved better detection performance and is, therefore, more suited for human detection tasks.

Human Detection in Intelligent Video Surveillance: A Review

2018 ◽  
Vol 22 (7) ◽  
pp. 1056-1064
Author(s):  
Li Hou ◽  
Qi Liu ◽  
Zhenhai Chen ◽  
Jun Xu ◽  
◽  
...  
Keyword(s):  
Video Surveillance ◽  
Rapid Development ◽  
Gaussian Mixture ◽  
Human Detection ◽  
Future Research ◽  
Intelligent Video Surveillance ◽  
Detection Techniques ◽  
The Core ◽  
Wide Range ◽  
Classifier Training

With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.

Design of Low-Cost Airport Boundary Intelligent Video Surveillance System

2013 ◽  
Vol 443 ◽  
pp. 228-232
Author(s):  
Hong Tao Liu
Keyword(s):  
Video Surveillance ◽  
Video Analysis ◽  
Motion Detection ◽  
Surveillance System ◽  
Low Cost ◽  
Practical Significance ◽  
Video Surveillance System ◽  
Intelligent Video Surveillance ◽  
Detection Technology ◽  
Intelligent Video Surveillance System

The existing Airport boundary intelligent video surveillance system is complicated to construct and costs a lot. This paper presents a design of economical Airport boundary intelligent video surveillance system according to the principle of optimization system and the resources share, combined the motion detection technology of NVR with intelligence video analyze equipment. The design can greatly decrease the false alarm rate and reduced the number of intelligence video analysis equipment. Therefor, it has higher application value and practical significance.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

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