Adaptive and efficient computer vision systems have been proposed to make computer
vision tasks, eg, object classification and object detection, optimized for embedded boards …

Recent object detection studies have been focused on video sequences, mostly due to the
increasing demand of industrial applications. Although single-image architectures achieve …

Object detection is considered one of the most challenging problems in this field of computer
vision, as it involves the combination of object classification and object localization within a …

While single-image object detectors can be naively applied to videos in a frame-by-frame
fashion, the prediction is often temporally inconsistent. Moreover, the computation can be …

Abstract This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack) a
novel video object detection framework for ultra-low-power embedded processors. This …

Transferring image-based object detectors to the domain of video remains challenging
under resource constraints. Previous efforts utilised optical flow to allow unchanged features …

This paper proposes an end-to-end deep learning framework, termed as motion-aid feature
calibration network (MFCN), for video object detection. The key idea is to leverage on the …

Extending state-of-the-art object detectors from image to video is challenging. The accuracy
of detection suffers from degenerated object appearances in videos, eg, motion blur, video …

Object detection in videos is an important task in computer vision for various applications
such as object tracking, video summarization and video search. Although great progress has …

With the development of deep neural networks, many object detection frameworks have
shown great success in the fields of smart surveillance, self-driving cars, and facial …