Object tracking by detection for video surveillance systems based on modified codebook foreground detection and particle filter

2011 ◽  
Author(s):  
Jiu Xu ◽  
Chenyuan Zhang ◽  
Satoshi Goto
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Video Surveillance ◽  
Foreground Detection ◽  
Surveillance Systems ◽  
Tracking By Detection

A New Modeling Approach for the Video Pre-Analysis of Video Surveillance Systems

2021 ◽  
Vol 12 (3) ◽  
pp. 21-34
Author(s):  
Hocine Chebi
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Analysis Method ◽  
Modeling Approach ◽  
Occluded Objects ◽  
Analysis Technique

The work presented in this paper aims to develop a new architecture for video surveillance systems. Among the problems encountered when tracking and classifying objects are groups of occluded objects. Simplifying the representation of objects leads to other reliable object tracking with smaller amounts of information used but protection of the necessary characteristics. Therefore, modeling moving objects into a simpler form can be considered a pre-analysis technique. Objects can be represented in different ways, and the choice of the representation of an object strongly depends on the field of application. An example of a video surveillance system respecting this architecture and using the pre-analysis method is proposed.

scholarly journals A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM

2020 ◽  
Vol 9 (4) ◽  
pp. 355-359
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Event Detection ◽  
Video Data ◽  
Surveillance Systems ◽  
Abnormal Event Detection ◽  
Suggested Approach ◽  
Processing Technologies ◽  
Safety Requirements ◽  
Convolutional Lstm

Today, due to public safety requirements, surveillance systems have gained increased attention. Video data processing technologies such as the identification of activity [1], object tracking [2], crowd counting [3], and the detection of anomalies [ 4] have therefore been rapidly developing. In this study, we establish an unattended method for the detection of anomaly events in videos based on a ConvLSTM encoder-decoder to learn about the evolution of spatial characteristics. Our model only covers typical video events during preparation, whereas in testing the videos are both usual and abnormal. Experiments on the UCSD datasets confirm the validity of the suggested approach to abnormal event detection.

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