Object Detection, Classification and Tracking Methods for Video Surveillance: A Review

Joshi, Rakesh Chandra; Joshi, M. S.; Singh, Adithya Gaurav; Mathur, Sanjay

doi:10.1109/ccaa.2018.8777708

Cited by 17 publications

(10 citation statements)

References 50 publications

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“…The first application is video surveillance systems used in traffic management and monitoring public places, which are essential in intelligent cities [35]. In this type of video, it is common to perform object detection [29] automated. However, human interaction still plays an essential role in their analy-sis, using visualizations as support [44].…”

Section: Related Workmentioning

confidence: 99%

MoReVis: A Visual Summary for Spatiotemporal Moving Regions

Giovani

Ferreira

Poco

2024

IEEE Trans. Visual. Comput. Graphics

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Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this paper, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

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Section: Related Workmentioning

confidence: 99%

MoReVis: A Visual Summary for Spatiotemporal Moving Regions

Giovani

Ferreira

Poco

2024

IEEE Trans. Visual. Comput. Graphics

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“…In recent years, there has been a significant influx of interest in smart surveillance systems [1], autonomous vehicles, self-driving cars, smart home systems, [2,3] and security. In particular, smart surveillance [4] can help us to improve traffic management.…”

Section: Introductionmentioning

confidence: 99%

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Raza¹,

Chelloug²,

Alatiyyah³

et al. 2023

Computers, Materials &Amp; Continua

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Pedestrian detection and tracking are vital elements of today's surveillance systems, which make daily life safe for humans. Thus, human detection and visualization have become essential inventions in the field of computer vision. Hence, developing a surveillance system with multiple object recognition and tracking, especially in low light and night-time, is still challenging. Therefore, we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night. In particular, we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared (IR) images using machine learning and tracking them using particle filters. Moreover, a random forest classifier is adopted for image segmentation to identify pedestrians in an image. The result of detection is investigated by particle filter to solve pedestrian tracking. Through the extensive experiment, our system shows 93% segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes. Moreover, the system achieved a detection accuracy of 90% using multiple template matching techniques and 81% accuracy for pedestrian tracking. Furthermore, our system can identify that the detected object is a human. Hence, our system provided the best results compared to the state-ofart systems, which proves the effectiveness of the techniques used for image segmentation, classification, and tracking. The presented method is applicable for human detection/tracking, crowd analysis, and monitoring pedestrians in IR video surveillance.

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“…These tracking methods can be divided into two types: generative methods and discriminative methods [7]. A generative method refers to describing an object to be tracked in a video through an object-representation method in computer vision and then extracting a corresponding object feature from a current frame containing the object to establish an object template [8][9]. The method then searches the subsequent frames for the area most similar to the object template and gradually iterates to finally achieve the positioning and tracking of the object in the subsequent frames [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

Xie

Liu

et al. 2020

IEEE Access

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In the field of correlation filter object tracking, the traditional template-update method easily causes template drift, so it performs poorly in complex scenes. To enhance the robustness of the template, a novel incremental multi-template update strategy is proposed in this paper. We find that reliability varies among all historical filters and that highly reliable filters are key to achieving accurate tracking. The incremental multi-template update strategy combines the local maximum-reliability filter template with the historical filter template incrementally, which is obviously different from the traditional update method. We apply this strategy to two trackers with superior performance. The experimental results of three test benchmarks, including the VOT2016, OTB100 and UAV123 datasets, show that the performance of our trackers is superior to that of the state-of-the-art trackers.

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Object Detection, Classification and Tracking Methods for Video Surveillance: A Review

Cited by 17 publications

References 50 publications

MoReVis: A Visual Summary for Spatiotemporal Moving Regions

MoReVis: A Visual Summary for Spatiotemporal Moving Regions

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

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