Mobile Video Object Detection with Temporally-Aware Feature Maps
arXiv:1711.06368
Abstract
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.
In CVPR 2018
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- Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
- Towards High Performance Video Object Detection for Mobiles
- Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation
- RetinaTrack: Online Single Stage Joint Detection and Tracking
- FASTER Recurrent Networks for Efficient Video Classification
- Fast Object Detection in Compressed Video
- PatchNet -- Short-range Template Matching for Efficient Video Processing