Illuminating Pedestrians via Simultaneous Detection & Segmentation (1706.08564v1)

Abstract: Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection. When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector. Using this approach, we find weakly annotated boxes to be sufficient for considerable performance gains. We provide an in-depth analysis to demonstrate how shared layers are shaped by the segmentation supervision. In doing so, we show that the resulting feature maps become more semantically meaningful and robust to shape and occlusion. Overall, our simultaneous detection and segmentation framework achieves a considerable gain over the state-of-the-art on the Caltech pedestrian dataset, competitive performance on KITTI, and executes 2x faster than competitive methods.

• The paper introduces a segmentation infusion network that integrates semantic segmentation with pedestrian detection using shared convolutional layers.
• The proposed SDS-RCNN architecture achieves a 23% reduction in detection errors and doubles execution performance on the Caltech dataset.
• The study demonstrates that weakly annotated boxes are sufficient for significant accuracy improvements, making the method practical for real-world applications.

Analyzing the Concept and Efficacy of Simultaneous Detection and Segmentation for Pedestrian Detection

The paper "Illuminating Pedestrians via Simultaneous Detection and Segmentation" by Garrick Brazil, Xi Yin, and Xiaoming Liu proposes a novel approach to enhance pedestrian detection in computer vision—an area of significant importance, particularly for applications like autonomous driving. The core innovation lies in integrating semantic segmentation into pedestrian detection processes to improve accuracy while maintaining network efficiency.

Key Contributions

The paper introduces a new architecture, termed as "segmentation infusion network," which combines both pedestrian detection and semantic segmentation to provide joint supervision. This architecture facilitates a holistic approach where the segmentation task refines the detection process by producing feature maps that are more semantically meaningful and robust against shape and occlusion of pedestrians.

1. Segmentation Infusion Layer: This layer is critical in the proposed method. It handles the integration of segmentation masks into shared layers of a convolutional neural network. The network, dubbed as Simultaneous Detection and Segmentation R-CNN (SDS-RCNN), effectively trains these shared layers to become more adept at the detection task without necessitating pixel-wise annotations, reducing dependency on high-quality segmentation labels, which are often unavailable in pedestrian datasets.
2. Framework Configuration: The paper utilizes Faster R-CNN as the baseline for detection, with enhancements like stricter supervision in the second-stage classifier and fusion of refined detection scores. Through this, the model reportedly achieves a leading performance on the Caltech dataset, marking a 23% reduction in error compared to other state-of-the-art methods and 2x better execution performance.
3. Weak Annotation Sufficiency: The research underlines that weakly annotated boxes are sufficient for achieving notable accuracy improvements. This is particularly relevant for datasets like Caltech and KITTI that lack pixel-wise annotation, making this approach both practical and adaptable.
4. Evaluation and Performance: The model was rigorously tested across standard datasets (Caltech and KITTI), achieving a significant advancement in reducing the pedestrian detection error rate. Quantitative results reflect SDS-RCNN’s competitive edge, emphasizing its capacity to handle the complexity of real-world scenarios with increased speed and precision.

Practical and Theoretical Implications

From a practical perspective, improving pedestrian detection accuracy is essential for advancing autonomous navigation systems, where ensuring pedestrian safety is paramount. The proposed framework not only enhances expressiveness in feature maps but also preserves network efficiency—qualities that are highly desirable in real-time applications.

Theoretically, the fusion of segmentation with detection challenges existing assumptions about their distinct roles in vision tasks. This research posits that by encouraging multi-task learning, one can achieve synergy where segmentation informs detection about finer details of pedestrian contours and presence, a departure from traditional models that treat these tasks independently.

Future Directions

The implications of this research are substantial not only for pedestrian detection but also for broader object detection tasks where segmentation could improve object boundary detections. The integration of multi-task learning for resource-constrained environments stands as a potential future research pathway. Additionally, exploring different network architectures or incorporating advanced pixel-wise imitation learning techniques could further improve performance without compromising efficiency.

In sum, the paper offers a significant step forward in refining the accuracy and efficiency of pedestrian detection methods through an innovative use of simultaneous detection and segmentation—a promising advancement that could see widespread utilization and adaptation in various computer vision applications.