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Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks ...
- Guruh Fajar Shidik, Edi Noersasongko, Adhitya Nugraha, Pulung Nurtantio Andono, Jumanto Jumanto, Edi...
- 2019
Jul 6, 2021 · Abstract. The latest popularity of computer vision, artificial intelligence, and connectivity between the systems and processes made a solid ground for the development of intelligent video surveillance (IVS). Intelligent Video Surveillance is a new research field with a substantial amount of new studies exploring its uses and potential.
- Romas Vijeikis, Vidas Raudonis, Gintaras Dervinis
- 2021
Mar 7, 2023 · A more efficient technique to guarantee safety and security in a variety of settings is through video surveillance, also known as closed circuit television (CCTV). It is frequently employed in strategic sectors, including security at home, public transportation, banks, and ATMs’ hubs, commercial districts, airports, and public roadways, and it is crucial for safeguarding crucial ...
- Region-Based Tracking
- Feature-Based Tracking
- Stage 2: Approaches of Object Tracking
- Stage 3: Multi-Object Tracking Classifiers
- Stage 4: Object Tracking Classification Methods
- Kalman Filter
- Particle Filter
- Bayesian Estimation
- Evaluation of Multi-Object Tracking
Region-based tracking algorithms track objects according to image regions identified by a previous segmentation of the moving objects. These methods assume that the foreground regions or blobs contain the object of interest. An example of region-based tracking is given by Wren et al. , where they used small blob features to track a person. Gaussian...
Feature-based tracking methods combine successive object detections for tracking. General local features are extracted, clustered for object classification, and then matched between images. Current methods can be classified into either causal or noncausal approaches. Noncausal approaches construct trajectories by finding the best association accord...
The approaches of object tracking models can be classified as shown in Figure 2.7. Tracking techniques can be categorized as off-line and online learning. 1. Off-Line Object Tracking It is the object tracking performed on pre-recorded video sequences where all the past and future frames are known already and can be accessed by the off-line models. ...
The classifier plays an important role in the overall performance of the tracking system. A poor classifier will lead to bad detection accuracy and false locks. Modules for object classification are important to identify regions of interest given by the visual observation module. In visual surveillance, this module classifies the moving objects int...
Object tracking classification methods have been mapped into Fig. 2.9, which are further discussed in the following sections. Tracking can be majorly classified into three categories, i.e., point tracking, kernel tracking, and silhouette tracking.
Anytime that the entity state is believed to be linear with Gaussian noise, the state of such a system can be calculated using the Kalman filter. The Kalman filter also has lower criteria for computation than the particle filter. Kalman filters are used to evaluate states in the state space format on the basis of linear dynamic systems . The phase ...
In real-life scenarios, though, the presumption that all state transformations are linear is very difficult. Therefore, the particle filter can be utilized for state estimation of the system due to its ability to represent random-state densities, and not just Gaussian . When the filter becomes usable in a regular preview loop, measurements are proc...
Online tracking of multi-objects using just one moving camera is a major challenge; because of global camera motion, the assumptions of 2D traditional motion models (e.g., models of first or second order) no longer hold in the picture coordinate. Recently, the Bayesian estimation methods have been advanced, in which a generalized spatiotemporal Gau...
To compute the training cost curve, the process is run several times with various detection score thresholds T. The common CLEAR MOT metrics are computed over this curve. The final scores are calculated using the field under these curves, which take into account the tracker’s output for all detector thresholds T. Intersection over union (IoU) of b...
Nov 1, 2022 · In this article, we address the introduction of AI-powered surveillance systems in our society by looking at the deployment of real-time facial recognition technologies (FRT) in public spaces and public health surveillance technologies, in particular contact tracing applications. Both cases of surveillance technologies assist public authorities ...
A range of video contents and technology have provided convenience to humans, with real-time video applications-such as surveillance applications-able to contribute to increasing public safety by reducing physical crimes. The development of video technology has made it possible to achieve an improved quality of life. However, this technology can also be exploited and lead to security issues ...
Aug 1, 2021 · Intelligent video surveillance by utilizing computer vision techniques for analyzing and understanding the lengthier video stream played a significant part in public security [3]. Since it is an essential element of smart video surveillance, abnormal activity identification automatically determines and recognize the abnormalities when observing an ever-changing scene and later time taken ...
