Detect to Track and Track to Detect (1710.03958v2)

Abstract: Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed.

Analysis of "Detect to Track and Track to Detect"

The paper "Detect to Track and Track to Detect" by Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman presents a unified ConvNet architecture designed to simultaneously address object detection and tracking in video sequences. Video-based object recognition often necessitates multi-stage pipelines, yet this approach introduces a more streamlined methodology that integrates both detection and tracking tasks into a cohesive framework, achieving competitive performance on the ImageNet VID dataset.

Contributions

The paper delineates three core contributions:

1. Integrated ConvNet Architecture: The researchers present a ConvNet architecture capable of performing detection and tracking concurrently using a multi-task objective approach. This involves frame-based object detection coupled with across-frame tracking regression, thereby reducing complexity and enhancing performance.
2. Correlation Features: By incorporating correlation features, the ConvNet is able to leverage object co-occurrences across temporal frames, improving its tracking abilities. These features enhance the alignment of detected objects across frames, aiding in the generation of more accurate and coherent tracking results.
3. Tracklet Linking for Video-Level Detection: The authors introduce a mechanism to link frame-level detections via tracklets, leading to improved video-level object detection accuracy. This process involves the use of tracklets to establish longer-term object trajectories or tubes across a video sequence.

Evaluation

The architecture was evaluated on the challenging ImageNet VID dataset, where it achieved state-of-the-art performance, outdoing the preceding year's ImageNet challenge winner. The methodology demonstrated superior single-model performance while maintaining conceptual simplicity. Additionally, the architecture allows for greater computational efficiency by increasing the temporal stride, significantly boosting tracking speed.

Experimental Results

The empirical results underscore the advantage of using a joint detection and tracking framework. The D{content}T model attained a 79.8% mAP on the ImageNet VID dataset, a notable improvement over existing methods. The enhancement in performance is attributed to the model's ability to mitigate typical video-specific challenges such as motion blur, occlusion, and unconventional poses.

Implications

The proposed D{content}T architecture holds significant implications for the development of future real-time applications in video analysis, given its simplicity and computational efficiency. The architecture's ability to improve detection accuracy with minimal overhead suggests potential applications in various domains, such as autonomous driving, surveillance, and augmented reality, where accurate and fast object detection is crucial.

Future Directions

The authors suggest further exploration of integrating multiple temporal strides in the analysis, which could harness even more spatiotemporal information and lead to improvements in model performance. Additionally, the architecture could benefit from exploring deeper neural network structures or alternative feature correlation methodologies to further enhance detection and tracking.

Conclusion

"Detect to Track and Track to Detect" is an insightful contribution to the field of video object detection, demonstrating a viable approach for combining detection and tracking into a unified framework. The paper's emphasis on simplifying the overall process while still achieving high accuracy marks it as a valuable resource for researchers focused on advancing object recognition in dynamic content. This work opens pathways for practical applications, indicating the potential for further advancements in video-based object recognition tasks.