WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems

Villamizar, Michael; Martínez-González, Ángel; Canévet, Olivier; Odobez, J-M.

doi:10.1109/avss.2018.8639165

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…For example, the research in (Lohani et al, 2021;Vijverberg et al, 2014;Zhang et al, 2015) aims to identify unauthorized objects within a protected outdoor area during specific periods. The unique challenges due to the use of the outdoor environment in this research, such as then eliminated by indoor changing weather conditions, fluctuating light levels, and the presence of insects and animals, are addressed by the indoor system (Matern et al, 2013;Villamizar et al, 2018). Another previous research use video anomaly detection, which identifies unusual attributes in appearance or motion within recorded videos (Feng et al, 2021;Li et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer

Erlina,

Fikri

2023

JAETS

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In Indonesia, assistance for small enterprises has grown in recent years. However, a monitoring system is required to support these enterprises and ensure their expansion and survival. Using a single-board computer and the YOLO algorithm, we construct a visitor tracking system in this study to meet this demand. To capture objects and categorize them as human or non-human, we employ the YOLOv4-tiny model, which has a mAP of 89.21%. Human visitors are welcomed with the use of a speaker. A telegraph bot that notifies the owner of the retail establishment of the visitor's presence also makes the presumption as to whether the visitor is a potential customer or an intruder. Our research demonstrates that the created monitoring system effectively recognizes and categorizes visits, enabling retail store owners to make defensible choices regarding visitor interaction and security precautions. Small business owners can save personnel costs while still maintaining high levels of client engagement and security. The theoretical application of this research is the creation of a visitor monitoring system that is affordable and may be used in small enterprises, particularly in Indonesia. The practical ramifications of our research include the possibility for small retail business owners to boost profits by lowering labor expenses while raising customer satisfaction and security. The importance of our study lies in its role in creating a monitoring system that will support small enterprises and increase their sustainability.

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Section: Resultsmentioning

confidence: 99%

A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer

Erlina,

Fikri

2023

JAETS

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“…The idea that this is an outside environment is crucial because, unlike an inside environment, it presents difficulties like varying weather conditions and light conditions, insects, animals, etc. [10,11].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach With Curated Dataset Generation for Enhanced Security

Pitafi,

Anwar,

Widia

et al. 2023

IEEE Access

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Perimeter intrusion detection systems (PIDS) play a crucial role in safeguarding critical infrastructures from unauthorized access and potential security breaches. Security is the main concern everywhere in the world. There are already many PIDS available, but the PID systems are still lacking in terms of probability of detection, false intrusion, and the activity recognition of intrusion. To solve the above problem, we designed a prototype for PIDS using a DHT22 temperature and humidity sensor, vibration sensor SW-420 Module Pinout, Mini PIR motion sensor, and Arduino UNO. After collecting the data from above mentioned sensors we applied machine learning algorithms DBSCAN to cluster the data points and K-NN classification to classify those clusters in one-dimensional data, but the results were not much satisfying. From there we got the motivation to improve the algorithm and applied it to two-dimensional data. The existing DBSCAN is not efficient due to its high complexity and the varying densities. To overcome these issues in this algorithm, we have improved the existing DBSCAN to ST-DBSCAN where we have used the estimation for the epsilon value and used the Manatton distance formula to find out the distance between points which produces 94.9853% accuracy on our dataset. Another contribution of the proposed work is that we have developed our own dataset named STPID-dataset, captured from security cameras installed in various locations which can be used by future researchers.INDEX TERMS Intrusion detection, perimeter intrusion detection system, machine learning, DBSCAN, intrusion activities. FIGURE.1.Illustration of perimeter intrusion detection [4]

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“…For RGB-D based human detection the multi-glimpse LSTM in [5] and an asymmetric adaptive fusion two-stream network (AAFTS-net, [6]) were proposed. For real-time people detection in top-view depth images from video surveillance systems, WatchNet and its extension WatchNet++ were presented in [7] and [8]. WatchNet consists of a feature extraction module and a series of prediction stages that sequentially refine the prediction maps for human body landmarks (head and shoulders) and is trained with artificial and real depth data for people detection.…”

Section: Introductionmentioning

confidence: 99%

“…Low-resolution depth images obtained by RGB-D sensors are used for privacy-preserving human pose estimation in [43] and for head detection in the task of counting boarding and alighting passengers [33]. Top-view depth images from a video surveillance system are employed for detecting people [7], as well as people committing attacks and intrusions [8].…”

Section: Introductionmentioning

confidence: 99%

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Seidel

Jahn

Seo

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers' privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx. 13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx. 96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.

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WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems

Cited by 9 publications

References 16 publications

A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer

A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer

Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach With Curated Dataset Generation for Enhanced Security

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

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