Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.object tracking

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    Multi-Vehicle Tracking and Speed Estimation Model using Deep Learning
    (Association for Computing Machinery, 2022) Prajwal, K.; Navaneeth, P.; Tharun, K.; Anand Kumar, M.A.
    Speed estimation of vehicles is one of the prime application of speed estimation of moving objects. The YOLOv5 model has proven to have a very good accuracy in detecting moving objects in real-time. The vehicles on the road are extracted from each frame of the video by running it through a custom YOLOv5 object detector. The YOLO model splits the frame into a grid and each grid detects a vehicle within itself. An instance identifier tracks the vehicle across the frames. The tracking algorithm computes deep features for every bounding box and utilizes the similarities within the deep features to identify and track the object. The pixel per meter metric has to adjusted based on perspective after which the speed of the vehicle can be estimated. Finally a comparison of our model metrics with the existing state of the art models is provided. © 2022 ACM.
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    Multi-Camera Multi-Person Tracking in Surveillance System
    (Institute of Electrical and Electronics Engineers Inc., 2023) Hedde, O.V.; Anand Kumar, M.
    Surveillance systems have become an integral part of modern security infrastructure. The ability to track multiple individuals across multiple cameras in real-time is crucial for the effectiveness of such systems. In this paper, we propose a multi-camera multi-person tracking system capable of accurately tracking multiple individuals across a network of cameras. Our proposed system utilizes a combination of computer vision and machine learning techniques to perform robust tracking in complex environments with varying lighting conditions, occlusions, and camera views. The system employs a deep learning-based object detection algorithm to detect individuals in each camera view and a multi-object tracking algorithm to associate and track the individuals across the camera network. To evaluate the performance of our proposed system, we conducted experiments on a publicly available dataset, and the results show the effectiveness of our system in achieving high accuracy and efficiency in multi-camera multi-person tracking. The proposed system is scalable and can be easily integrated with existing surveillance systems to enhance their tracking capabilities. Overall, our proposed multi-camera multi-person tracking system can provide an effective solution for the real-time tracking of individuals across a network of cameras, which can significantly enhance security and safety in various domains, such as transportation, public spaces, and critical infrastructure. © 2023 IEEE.