Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance

Wei, Haoran; Kehtarnavaz, Nasser

doi:10.3390/make1030044

Cited by 40 publications

(26 citation statements)

References 42 publications

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“…Human action recognition has been extensively studied in the literature and has already been incorporated into commercial products. There are a wide range of applications of human action recognition including human–machine interface, e.g., [1,2,3,4], intelligent surveillance, e.g., [5,6,7,8], content-based data retrieval, e.g., [9,10], and gaming, e.g., [11,12].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

Wei

Jafari

Kehtarnavaz

2019

Sensors

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This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered—Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.

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Section: Introductionmentioning

confidence: 99%

Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

Wei

Jafari

Kehtarnavaz

2019

Sensors

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“…With the recent advancement made in deep learning algorithms (in particular, convolutional neural networks), more effective solutions in terms of higher-accuracy object detection are reported in the literature. More specifically, two-stage detectors [ 32 , 33 , 34 , 35 , 36 , 37 ] are found to produce accurate detection outcomes. One-stage detectors [ 21 , 38 , 39 , 40 , 41 , 42 ] are introduced to gain computational efficiency.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Moving Object Detection in High-Resolution Video Sensing

Zhu

Wei

et al. 2020

Sensors

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This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

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“…The results of the individual detection indicate that YOLO-v2 can identify and classify objects with a high degree of accuracy from the aerial view. This [28] paper introduces a semi-supervised faster regionbased convolutional neural network (SF-RCNN) method for detecting people and classifying the load they carry in video data collected by high-power lens video cameras from distances of several miles. To detect areas that may contain a person.…”

Section: Human Detection and Re-identificationmentioning

confidence: 99%

Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

Khan

Saba

et al. 2021

IEEE Access

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Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is following the rules and regulations that prevent further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of corona virus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area on which the person touches more than to some specific threshold. We further mark that area to some particular color, which will help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on the object detection algorithms (YOLO, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 87.7% accuracy on person detection. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 81.7% accuracy using the confusion matrix.

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Semi-Supervised Faster RCNN-Based Person Detection and Load Classification for Far Field Video Surveillance

Cited by 40 publications

References 42 publications

Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

Fusion of Video and Inertial Sensing for Deep Learning–Based Human Action Recognition

Real-Time Moving Object Detection in High-Resolution Video Sensing

Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

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