A Review Paper on Face Detection and Recognition in Video

Chavan, Vishwanath D.; Bharate, Anil A

doi:10.17148/ijireeice.2016.4123

Cited by 3 publications

(2 citation statements)

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“…In recent years, various deep learning models developed based on CNNs have been demonstrated with strong robustness performance on image datasets such as ImageNet [49], which makes it the most popular deep learning framework in the area of computer vision [52]. One of the most popular applications is face recognition [53][54][55]. A basic CNN structure is shown in Figure 9.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Zhang

Aftab

Mihaylova

et al. 2022

Sensors

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Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Video face recognition (VFR) [53][54][55] deals with identifying an individual from the video data by comparing a given person's video with a library of still face images. In rail networks, VFR technologies can be used to automatically raise the alarm when the sensors detect an individual from a specific watchlist.…”

Section: Video Face Recognitionmentioning

confidence: 99%

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Zhang

Aftab

Mihaylova

et al. 2022

Sensors

View full text Add to dashboard Cite

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Towards Autonomous Depth Perception for Surveillance in Real World Environments

Behara

Chodavarapu

2017

2017 IEEE International Conference on Autonomic Computing (ICAC)

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The widespread emergence of human interactive systems has led to the development of portable 3D depth perception cameras. In this thesis, we aim to expand the functionality of surveillance systems by combining autonomous object recognition along with depth perception to identify an object and its distance from the camera. Specifically, we present an autonomous object detection method using the depth information obtained from the Microsoft Kinect sensor. We use the skeletal joints data obtained from Kinect V2 sensor to determine the hand position of people. The various hand gestures can be classified by training the system with the help of depth information generated by Kinect sensor. Our algorithm then works to detect and identify objects held in the human hand. The proposed system is compact, and the complete video processing can be performed by a low-cost single board computer.

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