scholarly journals Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sheng Ren ◽  
Jianqi Li ◽  
Tianyi Tu ◽  
Yibo Peng ◽  
Jian Jiang
Keyword(s):  
Object Detection ◽  
Super Resolution ◽  
Detection Algorithm ◽  
Public Security ◽  
Surveillance Video ◽  
Selection Algorithm ◽  
Keyframe Selection ◽  
Video Detection ◽  
Resolution Algorithm ◽  
Efficient Video

Video surveillance plays an increasingly important role in public security and is a technical foundation for constructing safe and smart cities. The traditional video surveillance systems can only provide real-time monitoring or manually analyze cases by reviewing the surveillance video. So, it is difficult to use the data sampled from the surveillance video effectively. In this paper, we proposed an efficient video detection object super-resolution with a deep fusion network for public security. Firstly, we designed a super-resolution framework for video detection objects. By fusing object detection algorithms, video keyframe selection algorithms, and super-resolution reconstruction algorithms, we proposed a deep learning-based intelligent video detection object super-resolution (SR) method. Secondly, we designed a regression-based object detection algorithm and a key video frame selection algorithm. The object detection algorithm is used to assist police and security personnel to track suspicious objects in real time. The keyframe selection algorithm can select key information from a large amount of redundant information, which helps to improve the efficiency of video content analysis and reduce labor costs. Finally, we designed an asymmetric depth recursive back-projection network for super-resolution reconstruction. By combining the advantages of the pixel-based super-resolution algorithm and the feature space-based super-resolution algorithm, we improved the resolution and the visual perception clarity of the key objects. Extensive experimental evaluations show the efficiency and effectiveness of our method.

scholarly journals A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhihong Li ◽  
Yang Dong ◽  
Yanjie Wen ◽  
Han Xu ◽  
Jiahao Wu
Keyword(s):  
Object Detection ◽  
Data Association ◽  
Detection Algorithm ◽  
Spatial Proximity ◽  
Public Security ◽  
Multitarget Tracking ◽  
Pedestrian Tracking ◽  
Tracking Accuracy ◽  
Data Set ◽  
The Government

Public security monitoring is a hot issue that the government and citizens pay close attention to. Multiobject tracking plays an important role in solving many problems for public security. Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. Emergencies usually have the characteristics of mutation, target diversification, low illumination, or resolution, which makes multitarget tracking more difficult. In this paper, we harness the advance of the deep learning framework for data association in object tracking by jointly modeling pedestrian features. The proposed deep pedestrian tracking SSD-based model can pair and link pedestrian features in any two frames. The model was trained with open dataset, and the results, accuracy, and speed of the model were compared between normal and emergency or violent environment. The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. In general, the model has good tracking results and credibility for multitarget tracking in emergency environment. The research provides technical support for safety assurance and behavior monitoring in emergency environment.

scholarly journals Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

2021 ◽  
Vol 10 (11) ◽  
pp. 742
Author(s):  
Xiaoyue Luo ◽  
Yanhui Wang ◽  
Benhe Cai ◽  
Zhanxing Li
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Moving Object Detection ◽  
Image Space ◽  
Moving Object ◽  
Surveillance Video ◽  
Traffic Surveillance

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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