Results 21 to 30 of about 6,103,782 (279)
Video object detection of the fully mechanized working face based on deep neural network
Gong-kuang zidonghua, 2022 The environment of the fully mechanized working face is complex. The terrain is long and narrow. The multi-object and multi-equipment often appear in the same scene, which makes object detection more difficult.
YANG Yi +4 more
doaj +1 more source
Object Detection in Traffic Videos: A Survey
IEEE Transactions on Intelligent Transportation Systems, 2022 <p>Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras.
Hadi Ghahremannezhad +2 more
openaire +2 more sources
Few-Shot Video Object Detection
, 2022 We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high ...
Qi Fan, Chi-Keung Tang, Yu-Wing Tai
openaire +3 more sources
Method for Fast Video Object Detection Based on Local Attention [PDF]
Jisuanji gongcheng, 2022 Video object detection is used to classify and locate targets in a video accurately.Existing video object detection methods based on deep learning propagate features through optical flow, which not only has the problem of a large number of model ...
SHI Yuhu, ZHANG Qigui
doaj +1 more source
Video foreground detection based on symmetric alpha-stable mixture models. [PDF]
, 2010 Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the
Bhaskar, H. +2 more
core +5 more sources
Insignificant shadow detection for video segmentation [PDF]
, 2005 To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames.
Tang, X. +14 more
core +1 more source
Object detection in sports videos
2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018 Object detection is commonly used in many computer vision applications. In our case, we need to apply the object detector as a prerequisite for action recognition in handball scenes. Object detection, to be successful for this task, should be as accurate as possible and should be able to deal with a different number of objects of various sizes ...
Matija Buric +2 more
openaire +2 more sources
Moving Shadow Detection in Video Using Cepstrum [PDF]
, 2010 Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels.
Cogun, Fuat, Cetin, Ahmet Enis
core +1 more source
Temporal-Guided Label Assignment for Video Object Detection
Applied Sciences, 2022 In video object detection, the deterioration of an object’s appearance in a single frame brings challenges for recognition; therefore, it is natural to exploit temporal information to boost the robustness of video object detection.
Shu Tian, Meng Xia, Chun Yang
doaj +1 more source
Video Compression for Object Detection Algorithms [PDF]
2018 24th International Conference on Pattern Recognition (ICPR), 2018 Video compression algorithms have been designed aiming at pleasing human viewers, and are driven by video quality metrics that are designed to account for the capabilities of the human visual system. However, thanks to the advances in computer vision systems more and more videos are going to be watched by algorithms, e.g.
Leonardo Galteri +3 more
openaire +2 more sources