Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Chenchen Fu, Yufei Cui, Lan Yi, Yuyang Sun
| Summary:
Real-time object detection takes an essential part in the decision-making
process of numerous real-world applications, including collision avoidance and
path planning in autonomous driving systems. This paper presents a novel
real-time streaming perception method named CorrDiff, designed to tackle the
challenge of delays in real-time detection systems. The main contribution of
CorrDiff lies in its adaptive delay-aware detector, which is able to utilize
runtime-estimated temporal cues to predict objects’ locations for multiple
future frames, and selectively produce predictions that matches real-world
time, effectively compensating for any communication and computational delays.
The proposed model outperforms current state-of-the-art methods by leveraging
motion estimation and feature enhancement, both for 1) single-frame detection
for the current frame or the next frame, in terms of the metric mAP, and 2) the
prediction for (multiple) future frame(s), in terms of the metric sAP (The sAP
metric is to evaluate object detection algorithms in streaming scenarios,
factoring in both latency and accuracy). It demonstrates robust performance
across a range of devices, from powerful Tesla V100 to modest RTX 2080Ti,
achieving the highest level of perceptual accuracy on all platforms. Unlike
most state-of-the-art methods that struggle to complete computation within a
single frame on less powerful devices, CorrDiff meets the stringent real-time
processing requirements on all kinds of devices. The experimental results
emphasize the system’s adaptability and its potential to significantly improve
the safety and reliability for many real-world systems, such as autonomous
driving. Our code is completely open-sourced and is available at
https://anonymous.4open.science/r/CorrDiff.
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