116 research outputs found
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
Mobile Video Object Detection with Temporally-Aware Feature Maps
This paper introduces an online model for object detection in videos designed
to run in real-time on low-powered mobile and embedded devices. Our approach
combines fast single-image object detection with convolutional long short term
memory (LSTM) layers to create an interweaved recurrent-convolutional
architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that
significantly reduces computational cost compared to regular LSTMs. Our network
achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate
feature maps across frames. This approach is substantially faster than existing
detection methods in video, outperforming the fastest single-frame models in
model size and computational cost while attaining accuracy comparable to much
more expensive single-frame models on the Imagenet VID 2015 dataset. Our model
reaches a real-time inference speed of up to 15 FPS on a mobile CPU.Comment: In CVPR 201
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