Automated Video Surveillance for Retail Store Statistics Generation

Avinash, N; Kumar, M. S. Shashi; Sagar, Stephen M.

doi:10.1007/978-81-322-0997-3_52

Cited by 6 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal is to create a 3D image of a person shot by multicameras that can involve a marker or not. Digital motion capture Due to the difficulty of a detailed 3D reconstruction of the human model, the outline (also called the visual hull) measures the 3D voxel estimate provided by each frame [18]. The gestures are then tracked and replicated in the human body model known as avatar.…”

Section: Figure 4 : Human Activities For Background Subtractionmentioning

confidence: 99%

Existing Structures, Problems and Future Directions in Background Subtraction of Practical Application

Logeswari*

2020

IJITEE

View full text Add to dashboard Cite

PC video-based vision frameworks additionally include the ID of moving articles in their first stage. Subtraction of the foundation is then applied to recognize the foundation from the frontal area. Setting subtraction in writing is unquestionably one of the most contemplated zones of PC vision and incorporates an immense number of productions. The majority of them are worried about adjusting scientific and AI strategies to be progressively effective in the game difficulties. A definitive objective, however, is that the setting subtraction approaches worked in research can be utilized in genuine applications for example, traffic checking. However taking a gander at the writing, we should see that there is frequently a distinction in basic research between the ebb and flow strategies utilized in genuine applications and the new techniques. Be that as it may, the recordings broke down in enormous scale archives are not far reaching in the sense they tended to only 50% of the full range of issues in genuine applications. In any case, taking a gander at the writing, we will take note of that there is frequently a distinction between the ebb and flow strategies utilized in genuine applications and the new techniques utilized in crucial research. In any case, the recordings broke down in enormous scale archives are not extensive in the sense they tended to only 50% of the full range of issues in genuine applications. We additionally recognize the foundation models that are utilized adequately in these applications to distinguish potential usable new foundation models regarding heartiness, time and memory necessity.

show abstract

Section: Figure 4 : Human Activities For Background Subtractionmentioning

confidence: 99%

Existing Structures, Problems and Future Directions in Background Subtraction of Practical Application

Logeswari*

2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…The detection and tracking of foreground objects of interest such as people, vehicles, and abandoned objects are important in order to assure social security [95], [193].…”

Section: A Intelligent Visual Surveillance Of Human Activitiesmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

View full text Add to dashboard Cite

Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

show abstract

“…Human activities can be also monitored in maritime scenes to count the number of ships which circulated in a marina or in a harbor [200][295] [416][112], and to detect and track ships in fluvial canals. Other environments are store scenes for the detection and tracking of consumers [212][211][210] [16]. 2.…”

Section: Visual Surveillance Of Human Activitiesmentioning

confidence: 99%

“…By tracking the path traveled by the customer along the store, important pieces of information, such as customer dwell time and product interaction statistics can be collected. One of the most important customer statistics is the information about the shopper groups such as in Leykin and Tuceryan [212][211] [210] and Avinash et al [16]. Table 8 shows an overview of the different publications in the field of store surveillance with information about the background model, the background maintenance, the foreground detection, the color space and the strategies used by the authors.…”

Section: Store Surveillancementioning

confidence: 99%

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Bouwmans

García-García²

2019

Preprint

View full text Add to dashboard Cite

Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.

show abstract

Automated Video Surveillance for Retail Store Statistics Generation

Cited by 6 publications

References 6 publications

Existing Structures, Problems and Future Directions in Background Subtraction of Practical Application

Existing Structures, Problems and Future Directions in Background Subtraction of Practical Application

A Comprehensive Survey of Video Datasets for Background Subtraction

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Contact Info

Product

Resources

About