Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach

• URL: http://arxiv.org/abs/2105.08253v1
• Date: Tue, 18 May 2021 03:22:03 GMT
• Title: Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach
• Authors: Ryota Yoshihashi, Rei Kawakami, Shaodi You, Tu Tuan Trinh, Makoto Iida, Takeshi Naemura
• Abstract summary: We present a neural network model called the Recurrent Correlational Network, where detection and tracking are jointly performed. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements. Our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on a bird image dataset.
• Score: 19.59528430884104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable. Specifically, the challenge includes very low resolution of the objects, MPEG artifacts due to compression and a large searching area with many hard negatives. Tracking is equally difficult because of the unreliable appearance, and the unreliable motion estimation. Luckily, we found that by combining this two challenging tasks together, there will be mutual benefits. Following the idea, in this paper, we present a neural network model called the Recurrent Correlational Network, where detection and tracking are jointly performed over a multi-frame representation learned through a single, trainable, and end-to-end network. The framework exploits a convolutional long short-term memory network for learning informative appearance changes for detection, while the learned representation is shared in tracking for enhancing its performance. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements in detection performance over deep single-frame detectors and existing motion-based detectors. Furthermore, our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on a bird image dataset.

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