Real-time Object Detection for Streaming Perception (2203.12338v2)

Abstract: Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment after processing, streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem. We build a simple and effective framework for streaming perception. It equips a novel DualFlow Perception module (DFP), which includes dynamic and static flows to capture the moving trend and basic detection feature for streaming prediction. Further, we introduce a Trend-Aware Loss (TAL) combined with a trend factor to generate adaptive weights for objects with different moving speeds. Our simple method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline, validating its effectiveness. Our code will be made available at https://github.com/yancie-yjr/StreamYOLO.

Analysis of "Real-time Object Detection for Streaming Perception"

The paper "Real-time Object Detection for Streaming Perception" addresses the challenge of maintaining low-latency and high-accuracy object detection in autonomous driving scenarios, focusing on the problem of real-time video perception. Previous approaches to this task often dealt with trade-offs between speed and accuracy. However, this paper identifies future prediction in real-time models as a pivotal solution to improve streaming perception without compromising on either accuracy or latency.

Key Contributions and Methodology

The authors introduce a novel framework for streaming perception, embedding a Dual-Flow Perception (DFP) module and a Trend-Aware Loss (TAL) to enhance predictive capabilities. The framework is evaluated on the Argoverse-HD dataset, where it reports a substantial improvement of 4.9% in Average Precision (AP) compared to strong baselines. This signals notable progress and effectiveness of the proposed methodologies.

Framework and Design Innovations:

1. Dual-Flow Perception Module (DFP): The DFP module integrates dynamic and static flows. The dynamic flow captures motion trends and the static flow preserves detection features, facilitating predictive accuracy across consecutive frames. The dual-flow design assists in addressing the misalignment between processed frames and their subsequent real-time status, especially vital in fast-changing environments such as those faced by autonomous vehicles.
2. Trend-Aware Loss (TAL): TAL introduces an adaptive weighting strategy based on object movement speed, giving higher importance to swiftly moving objects. This is computed using a trend factor derived from the difference in object positions across frames. As a result, the model dynamically adjusts focus during training, leading to more accurate predictions of objects' future states.

Results and Implications

The paper's empirical validation highlights the robustness and competitive nature of the proposed framework. Using real-time detectors like YOLOX, the integration of future prediction into the perception stack has shown to significantly bridge the performance gap between offline and online settings. The proposed method effectively addresses the latency and consistency challenge inherent in streaming perception, paving the way for more reliable autonomous driving systems.

Theoretical Implications:

• The integration of dynamic prediction highlights a shift in perception model design where rather than static observation, learning models are now adapting to predict moving trends. This points towards a broader application in dynamic machine learning tasks across various domains beyond autonomous vehicles.

Practical Implications:

• In practice, the ability to predict object movement can prevent unsafe driving decisions that might occur due to latency, improving the application viability of real-time perception systems in on-road autonomous driving.

Future Directions:

The paper indicates that despite the promising results, there is room for further improvement in forecasting accuracy relative to the offline benchmarks. Future research could explore the integration of more advanced temporal modeling such as recurrent networks or transformers, potentially improving the adaptation to complex scene dynamics. Additionally, considering hardware constraints, optimizing the computational efficiency of such predictive models could facilitate deployment in resource-constrained environments.

Conclusion

This paper provides an insightful approach to enhance streaming perception for autonomous driving by focusing on predicting future object states. The DFP module and TAL constitute a significant methodological advancement that contributes to narrowing the performance gap between real-time and offline processing. While demonstrating robustness, the approach invites further exploration into integration with more sophisticated algorithms and real-world application testing, driving forward the capabilities of real-time perception systems.