DeepScale: An Online Frame Size Adaptation Framework to Accelerate Visual Multi-object Tracking

• URL: http://arxiv.org/abs/2107.10404v1
• Date: Thu, 22 Jul 2021 00:12:58 GMT
• Title: DeepScale: An Online Frame Size Adaptation Framework to Accelerate Visual Multi-object Tracking
• Authors: Keivan Nalaie, Rong Zheng
• Abstract summary: DeepScale is a model agnostic frame size selection approach to accelerate tracking throughput. It can find a suitable trade-off between tracking accuracy and speed by adapting frame sizes at run time. Compared to a state-of-the-art tracker, DeepScale++, a variant of DeepScale achieves 1.57X accelerated with only moderate degradation.
• Score: 8.878656943106934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In surveillance and search and rescue applications, it is important to perform multi-target tracking (MOT) in real-time on low-end devices. Today's MOT solutions employ deep neural networks, which tend to have high computation complexity. Recognizing the effects of frame sizes on tracking performance, we propose DeepScale, a model agnostic frame size selection approach that operates on top of existing fully convolutional network-based trackers to accelerate tracking throughput. In the training stage, we incorporate detectability scores into a one-shot tracker architecture so that DeepScale can learn representation estimations for different frame sizes in a self-supervised manner. During inference, based on user-controlled parameters, it can find a suitable trade-off between tracking accuracy and speed by adapting frame sizes at run time. Extensive experiments and benchmark tests on MOT datasets demonstrate the effectiveness and flexibility of DeepScale. Compared to a state-of-the-art tracker, DeepScale++, a variant of DeepScale achieves 1.57X accelerated with only moderate degradation (~ 2.4) in tracking accuracy on the MOT15 dataset in one configuration.

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