- The paper's main finding is that pedestrian detectors specialized for specific datasets fail to generalize to unseen data.
- Comprehensive cross-dataset evaluations show general-purpose object detectors outperform specialized models in real-world scenarios.
- The study advocates a progressive training pipeline using diverse, high-density datasets to enhance detection robustness.
Generalizable Pedestrian Detection: The Elephant In The Room
The paper "Generalizable Pedestrian Detection: The Elephant In The Room" addresses a critical issue in the field of pedestrian detection within computer vision: the generalization capabilities of existing pedestrian detectors. Despite current detectors achieving high performance on specific datasets, their ability to generalize to unseen data remains an open question. This paper systematically investigates this issue through comprehensive cross-dataset evaluations, shedding light on the limitations of popular state-of-the-art pedestrian detectors.
Pedestrian detection plays a pivotal role in various real-world applications, including autonomous driving, video surveillance, and action recognition. Traditionally, the efficacy of pedestrian detectors has been predominantly assessed using within-dataset evaluation, where both training and testing occur on the same dataset. The authors argue that this practice leads to overfitting and limits the real-world applicability of these models.
A key revelation from this paper is that while existing detectors perform well on individual benchmarks, they falter in cross-dataset evaluations. Two primary reasons are identified for this discrepancy: the inherent bias in detector designs—such as anchor settings tailored for specific datasets—and the lack of pedestrian density and scene diversity in training datasets. The authors highlight the surprising observation that general-purpose object detectors, devoid of pedestrian-specific adaptations, demonstrate superior generalization across different datasets compared to specialized pedestrian detectors.
The paper further explores how training on diverse and densely populated datasets, collected via web crawling, can boost generalization capabilities. Datasets such as CrowdHuman and Wider Pedestrian are shown to enhance the robustness and adaptability of pedestrian models. The authors propose a progressive training pipeline, which incrementally fine-tunes models starting from a broad domain and targeting more specific domains. Employing this strategy, they achieve significant performance improvements in pedestrian detection, particularly in autonomous driving scenarios.
Experimental results underscore the importance of evaluating models on more diverse datasets. A comprehensive comparison across various models—namely BGCNet, CSP, PRNet, ALFNet, and Cascade R-CNN—reveals that general detectors often outperform pedestrian-specific models in cross-dataset evaluation settings. Notably, while within-dataset evaluations favor pedestrian-specialized designs, cross-dataset evaluations prove general detectors' superior adaptability.
The implications of this paper are substantial for future advancements in AI-based detection systems. Emphasizing cross-dataset evaluation provides a more realistic assessment of model robustness, essential for practical deployment in dynamic and unpredictable environments. The findings suggest that developers should pivot towards creating detectors with broader applicability, rather than exclusively fine-tuning for individual benchmarks.
Looking ahead, the research invites further investigation into creating even more generalized and adaptable models, potentially leveraging unsupervised or few-shot learning techniques to tackle domain shifts more effectively. As pedestrian detection technology advances, such strategic shifts in evaluation and development practices could lead to safer and more reliable applications, particularly in safety-critical domains like autonomous vehicles.