24,023 research outputs found
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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