To address the challenges of non-cooperative and remote human activity detection, a multimodal remote audio/video acquisition system is developed. The system mainly consists of a Pan-Tilt-Zoom (PTZ) camera and a Laser...
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To address the challenges of non-cooperative and remote human activity detection, a multimodal remote audio/video acquisition system is developed. The system mainly consists of a Pan-Tilt-Zoom (PTZ) camera and a Laser Doppler Virbometer (LDV). The traditional all-fiber structure has residual carriers, which degrades the system performance badly. To solve the problem, a partial-fiber LDV is developed to obtain remote audio by detecting the vibration of the object (caused by the acoustic pressure around the target). Besides, to improve the quality of LDV audio signals, a speech enhancement algorithm (OM-LSA) is applied to remove noises in the LDV audio signals. The PTZ camera can provide remote visual information. We also use the yolo algorithm to discriminate human from the photos which are updated from the PTZ camera continuously. That is the primary application of the yolo algorithm. Moreover, the yolo algorithm is used to recognize the objects around the target person by processing the video signals acquired by PTZ camera, which can aid the LDV in finding a suitable vibration target. In experiments, we show that the remote (50 m) speech signals and visual signals can be obtained by this surveillance system. That means this system has the ability to detect remote human activities.
Vision-based detection methods often require consideration of the robot's sight. For example, panoramic images cause image distortion, which negatively affects the target recognition and spatial localization. Furt...
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Vision-based detection methods often require consideration of the robot's sight. For example, panoramic images cause image distortion, which negatively affects the target recognition and spatial localization. Furthermore, the original you only look once method does not have a reasonable performance for the image recognition in the panoramic images. Consequently, some failures have been reported so far when implementing the visual recognition on the robot. In the present study, it is intended to optimize the conventional you only look once algorithm and propose the modified you only look once algorithm. Comparing the obtained results with the experiment shows that the modified you only look once method can be effectively applied in the graphics processing unit to reach the panoramic recognition speedup to 32 frames rate per second, which meets the real-time requirements in diverse applications. It is found that the accuracy of the object detection when applying the proposed modified you only look once method exceeds 70% in the studied cases.
The air pollution these days is a serious environmental concern and it is not just a mere fact but a harsh reality which is creating problems for the mankind such as some serious health issues. In some parts of the wo...
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The air pollution these days is a serious environmental concern and it is not just a mere fact but a harsh reality which is creating problems for the mankind such as some serious health issues. In some parts of the world the air quality index have reached to an irrefutable level which demands for a solution now. Hence, the purpose of this paper is to tackle this problem at a large scale by providing with an artificially intelligent mobile air purifier for “outdoors”. The main problem of DMAPS is to provide with purified air in the outdoors, such as a residential colony, in apartments, office complex, etc. since most of the times people travel outdoors and stay away from their home for a long time. This ability of providing with such a smart-sensing, self-driving machine is the advantage of the paper because other air purifiers exist but they are made for indoors, they do not have mobility and also do not possess AI capabilities. The method used in this air purifier is formulated by connecting three major functions. The first one being, ‘the smart mobility’ of the machine using the approach of deep reinforcement learning, the second is the air ‘purification using the Arduino system’ and the last is, ‘the detection of humans’ so that DMAPS can be around them and provide them with purified air, based on the yolo algorithm. The whole system, works in a way that each and every function is very much connected and works simultaneously. In a nutshell, this air purifier, moves from one place (where there is low pollution content) to another place (where there is higher pollution content), purifies the environment and simultaneously have an objective to smartly detect humans to give out fresh air to them. Hence, in this way the whole methodology tries to fix the problem of air pollution by purifying the air outdoors to make it safe to inhale.
There is a huge demand of video surveillance based intelligent security systems which can automatically detect the unauthorized entry or mal-intentional intrusion to the unattended sensitive areas and notify to the co...
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ISBN:
(纸本)9781728137155
There is a huge demand of video surveillance based intelligent security systems which can automatically detect the unauthorized entry or mal-intentional intrusion to the unattended sensitive areas and notify to the concerned authorities in real-time. A novel video-based Intrusion Detection System (IDS) using deep learning is proposed. Here, You Only Look Once (yolo) algorithm is used for object detection and intrusion is decided using our proposed algorithm based on the shifted center of mass of the detected object. Further, Simple Online and Real-time Tracking (SORT) algorithm is used for the tracking of the intruder in real-time. The developed system is also implemented and tested for live video stream using NVIDIA Jetson TX2 development platform with an accuracy of 97% and average fps of 30. Here, the proposed IDS is a generic one where the user can select the region of interest (the area to be intrusion free) of any size and shape from the reference (starting) frame and potential intruders such as a person, vehicle, etc. from the list of trained object classes. Hence, it can have a wide range of smart city applications such as person intrusion free zone, no vehicle entry zone, no parking zone, smart home security, etc.
Deep learning methods, in their latest progress, enable key steps in object detection and recognition to be performed with convolutional neural networks (CNN). In this article, we apply yolo (You Only Look Once) algor...
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ISBN:
(数字)9781728158068
ISBN:
(纸本)9781728158075
Deep learning methods, in their latest progress, enable key steps in object detection and recognition to be performed with convolutional neural networks (CNN). In this article, we apply yolo (You Only Look Once) algorithm on areal imagery for spatiotemporal tracking of vehicles, for GIS applications. Data augmentation technics are used to increase the number of training and test data. After augmenting the aerial images of vehicles and their corresponding labels, yolo is trained in iterations until reaching an acceptable precision. Then the trained algorithm is applied on frames of an aerial video of a parking lot, whose outputs (bounding boxes) are used in the LinktheDot algorithm in order to track vehicles in space and time.
Because the image of UAV aerial photography is easy to be affected by light, sea area and other conditions, there are many kinds of ships. Under different conditions, the characteristics of ships are different, which ...
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ISBN:
(数字)9781728160672
ISBN:
(纸本)9781728160689
Because the image of UAV aerial photography is easy to be affected by light, sea area and other conditions, there are many kinds of ships. Under different conditions, the characteristics of ships are different, which makes the target recognition more difficult. In order to improve the efficiency of sea surface supervision and make the sea surface management more intelligent, an ocean ship detection algorithm based on aerial photography image is proposed. In this paper, the improved yolo algorithm is mainly used for high-efficiency ship detection of aerial video, which can achieve real-time performance and detection speed of 23fps. In order to improve the accuracy, this paper proposes a standardized mechanism of fixed frame length detection results, which uses deep learning mask RCNN algorithm for fine detection of specific frame images, and the detection map is 85%, which improves the detection speed without affecting the detection speed The accuracy of the algorithm forms an efficient and accurate algorithm for the detection of ships on the sea, which brings convenience to the management of the sea.
When it comes to vehicles and traffic, the first priority is the safety of the driver and that of pedestrians. With the evolution of technology, the speed of the cars and the traffic increased. This means that there i...
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ISBN:
(纸本)9781538650622
When it comes to vehicles and traffic, the first priority is the safety of the driver and that of pedestrians. With the evolution of technology, the speed of the cars and the traffic increased. This means that there is a need to predict dangers which can arise while someone is driving. Another direction is to develop autonomous cars that can face the present and the future traffic conditions in order to increase the safety and the fluency on the roads. So, in the present paper it is presented a method of determining the distance to a car that drives in front of our own car. The relative speed is also possible to be determined with respect to it. The image, taken with a front camera, is classified using a Convolutional Neural Network, namely yolo [1] (You Only Look Once) and after the searched object is detected, the distance is estimated counting the number of pixels in a bounding box which fits the detected object. The distance is further corrected by using Canny edge detection and HSV color space. Experimental data are presented in the paper and the results are commented for conclusions.
In order to provide a notification system for the blind awaiting a bus, a classification of viewpoints is necessary. In a previous study, we classified the viewpoints in non-congested traffic using road features extra...
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ISBN:
(纸本)9781728119960
In order to provide a notification system for the blind awaiting a bus, a classification of viewpoints is necessary. In a previous study, we classified the viewpoints in non-congested traffic using road features extraction. However, the viewpoints in congested traffic have not yet been discussed;hence this paper proposes a viewpoints classification using car distribution on the road in congested traffic. The study aimed to classify the viewpoints to two classes as "Good Viewpoints" and "Bad Viewpoints" with 100 images for each class. The experimental results showed a 76% accuracy of the proposed method.
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