Temporal-Fluctuation-Reduced Video Encoding for Object Detection in Wireless Surveillance Systems

2016 ◽  
Author(s):  
Lingchao Kong ◽  
Rui Dai
Keyword(s):  
Object Detection ◽  
Video Encoding ◽  
Surveillance Systems ◽  
Temporal Fluctuation

scholarly journals IoT Driven Automated Object Detection Algorithm for Urban Surveillance System in Smart City

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1687-1691
Keyword(s):  
Object Detection ◽  
Road Traffic ◽  
Smart Cities ◽  
Digital Camera ◽  
Detection Algorithm ◽  
License Plate ◽  
Surveillance Systems ◽  
Research Issues ◽  
Automated Object Detection ◽  
Research Challenge

Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoTdriven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. The other sensors like flame and ultrasonic sensor are used to monitor nearby objects. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban 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.

scholarly journals Understanding Ocean Acoustics by Eigenray Analysis

2019 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Jens M. Hovem ◽  
Hefeng Dong
Keyword(s):  
Object Detection ◽  
Underwater Acoustics ◽  
Acoustic Propagation ◽  
Surveillance Systems ◽  
Ocean Acoustics ◽  
Underwater Acoustic ◽  
Acoustic Modelling

Acoustics is important for all underwater systems for object detection, classification, surveillance systems, and communication. However, underwater acoustics is often difficult to understand, and even the most carefully conducted measurements may often give unexpected results. The use of theory and acoustic modelling in support of measurements is very important since theory tends to be better behaved and more consistent than experiments, and useful to acquire better knowledge about the physics principle. This paper, having a tutorial flair, concerns the use of ray modelling and in particular eigenray analysis to obtain increased knowledge and understanding of underwater acoustic propagation.

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