This paper proposes a method exploiting temporal context with an attention mechanism for
detecting objects in real-time in a live streaming video. Video object detection is challenging …

Object detection in videos is a fundamental technology for applications such as monitoring.
Since video frames are treated as independent input images, static detectors ignore the …

Video object detection enhances the performance of the still-image based object detection
by exploiting temporal context information from neighboring frames. Most of the state-of-the …

The deep learning technique has recently led to significant improvement in object-detection
accuracy. Numerous object detection schemes have been designed to process each frame …

We present a new network architecture able to take advantage of spatio-temporal
information available in videos to boost object detection precision. First, box features are …

This paper proposes a method that jointly learns to detect objects at the current frame and
forecast the next frame's future feature map. Previous offline detectors have shown the …

How to exploit spatio-temporal information in video to improve the object detection precision
remains an open problem. In this paper, we boost the object detection accuracy in video with …

Abstract We introduce Spatial-Temporal Memory Networks for video object detection. At its
core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent …

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 …

Video object detection is challenging and essential in practical applications, such as
surveillance cameras for traffic control and public security. Unlike the video in natural …