48,469 research outputs found

    Deep Learning based Animal Detection and Tracking in Drone Video Footage

    Get PDF
    In this paper, we propose a multiple animal tracking system in drone footage that is designed and implemented using a Deep Neural Network (DNN) based tracking-by-detection approach. The proposed system consists of two main components, namely the sub-system for animal detection, and the sub-system for animal tracking. In the animal detection component, we exploit the effective use of YOLO-V5 to detect individual animals and in the tracking component, we use a centroid tracking algorithm to associate the location of the detected animals in consecutive video frames. The performance of the proposed system is analyzed on drone video footage containing herds of Arabian Oryx with complex patterns of movement of individual animals. All videos were recorded by using a drone flying over known oryx feeding points in the desert areas of the UAE. The experimental results showed that our tracking system can detect and track individual oryxes within herds, accurately, even when the oryxes are very close to each other, partially occluded and their walking paths cross each other

    Continuous Gaze Tracking With Implicit Saliency-Aware Calibration on Mobile Devices

    Full text link
    Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user's cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user's gaze. vGaze is implemented as a light-weight software that identifies video frames with "useful" saliency information, sensing the user's head movement, performs opportunistic calibration using only those "useful" frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51 cm (2.884 degree) which decreases to 0.99 cm (1.891 degree) with historical information and 0.57 cm (1.089 degree) with an indicator

    Object Detection in Videos with Tubelet Proposal Networks

    Full text link
    Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos is vital for object detection. To fully utilize temporal information, state-of-the-art methods are based on spatiotemporal tubelets, which are essentially sequences of associated bounding boxes across time. However, the existing methods have major limitations in generating tubelets in terms of quality and efficiency. Motion-based methods are able to obtain dense tubelets efficiently, but the lengths are generally only several frames, which is not optimal for incorporating long-term temporal information. Appearance-based methods, usually involving generic object tracking, could generate long tubelets, but are usually computationally expensive. In this work, we propose a framework for object detection in videos, which consists of a novel tubelet proposal network to efficiently generate spatiotemporal proposals, and a Long Short-term Memory (LSTM) network that incorporates temporal information from tubelet proposals for achieving high object detection accuracy in videos. Experiments on the large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed framework for object detection in videos.Comment: CVPR 201
    • …
    corecore