Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Yufei Cui, Chenchen Fu, Weiwei Wu, Zihao Wang
| Summary:
Real-time object detection is critical for the decision-making process for
many real-world applications, such as collision avoidance and path planning in
autonomous driving. This work presents an innovative real-time streaming
perception method, Transtreaming, which addresses the challenge of real-time
object detection with dynamic computational delay. The core innovation of
Transtreaming lies in its adaptive delay-aware transformer, which can
concurrently predict multiple future frames and select the output that best
matches the real-world present time, compensating for any system-induced
computation delays. The proposed model outperforms the existing
state-of-the-art methods, even in single-frame detection scenarios, by
leveraging a transformer-based methodology. It demonstrates robust performance
across a range of devices, from powerful V100 to modest 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, Transtreaming 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.
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