Multimodal Abandoned/Removed Object Detection for Low Power Video Surveillance Systems

2009 ◽  
Author(s):  
Michele Magno ◽  
Federico Tombari ◽  
Davide Brunelli ◽  
Luigi Di Stefano ◽  
Luca Benini
Keyword(s):  
Low Power ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance Systems

scholarly journals A deep learning approach to building an intelligent video surveillance system

2020 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Single Shot ◽  
Convolutional Networks ◽  
Starting Point ◽  
Intelligent Video Surveillance System

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

Moving object detection using unstable camera for video surveillance systems

Optik ◽  
2015 ◽  
Vol 126 (20) ◽  
pp. 2436-2441 ◽  
Author(s):  
Seungwon Lee ◽  
Nahyun Kim ◽  
Kyungwon Jeong ◽  
Kyungju Park ◽  
Joonki Paik
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Moving Object Detection ◽  
Moving Object ◽  
Surveillance Systems

scholarly journals Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review

2018 ◽  
Vol 27 (02) ◽  
pp. 1830001 ◽  
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Visual Surveillance ◽  
Surveillance Systems ◽  
Objects Tracking ◽  
Object Appearance ◽  
Human Operators ◽  
Active Research

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance.

Object detection and tracking for low-cost video surveillance system

2013 ◽  
Vol 18 (2-3) ◽  
pp. 91-99
Author(s):  
Jaromir Przybylo
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Low Cost ◽  
Elderly Person ◽  
Video Stream ◽  
Modern World ◽  
False Alarms ◽  
Surveillance Systems ◽  
Unusual Behavior

Abstract Automated and intelligent video surveillance systems play important role in the modern world. Since the amount of various video streams that must be analyzed grows, such artificial intelligence systems can assist humans in performing tiresome tasks. As a result, the effectiveness of response to a dangerous situations is increasing (detect unexpected movement or unusual behavior that may pose a threat to people, property and infrastructure). Video surveillance systems have to meet several requirements: must be accurate and not produce too many false alarms, moreover it must be able to process the received video stream in real-time to provide a sufficient response time. The work presented here focuses on the selected challenges of scene analysis in video surveillance systems (object detection/tracking, effectiveness of the whole system). The aim of the research is to design a low-budget surveillance system, that can be used for example in a home security monitoring. Such solution can be use not only to surveillance but also to monitor elderly person at home or provide new ways of interacting in human-computer interaction systems.

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