The EuroCity Persons Dataset: A Novel Benchmark for Object Detection (1805.07193v2)

Abstract: Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238200 person instances manually labeled in over 47300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day- vs. night-time, geographical region), the dataset detail (i.e. availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.

• The paper introduces the EuroCity Persons dataset with over 238,200 person instances to benchmark object detection in diverse urban settings.
• It employs detailed annotations, including occlusion, truncation, and orientation, to enhance model generalization for real-world applications.
• Experiments with detectors like Faster R-CNN demonstrate improved accuracy, achieving a log average miss rate as low as 8.1% under standard conditions.

The EuroCity Persons Dataset: A Landmark in Object Detection Benchmarking

The paper "The EuroCity Persons Dataset: A Novel Benchmark for Object Detection" introduces a substantial contribution to the domain of computer vision with a focus on object detection in urban environments. The authors, Markus Braun, Sebastian Krebs, Fabian Flohr, and Dariu M. Gavrila, present the EuroCity Persons dataset, which is distinguished by its considerable size, diverse annotations, and applicability for assessing object detection algorithms.

The EuroCity Persons dataset stands out due to its extensive scale, comprising over 238,200 person instances across 47,300 images. The data was collected from on-board cameras in 31 cities across 12 European countries, offering a diverse array of urban environments and situations. This large geographic representation contributes significantly to its dataset diversity—a factor crucial for robust model generalization. In context with other datasets like Caltech and KITTI, EuroCity Persons is nearly an order of magnitude larger in terms of person annotations.

This dataset is not only large but also rich in detail, featuring pedestrian annotations alongside cyclist and other rider data. It includes detailed metadata like person orientation, making it particularly beneficial for applications that require nuanced data, such as intelligent vehicles and robot navigation systems. The explicit annotations for both occlusion and truncation enhance the dataset’s utility for real-world applications.

The authors evaluated state-of-the-art object detection methods, namely Faster R-CNN, R-FCN, SSD, and YOLOv3, using the dataset. The results highlight the importance of well-optimized, large-scale datasets for improving detection performance. The experiments revealed that even with current advancements, increasing the dataset's scale continues to yield improvements in detection accuracy. Faster R-CNN was observed to provide the best results, with log average miss rates (LAMR) of 8.1% for the reasonable size test scenario, showing a significant benchmark for further enhancements in the field.

Experimentation extended beyond simple benchmarking to include an analysis of the impact of dataset characteristics such as size, diversity, annotation detail, and quality on object detector performance. The dataset's ability to record day and night-time scenarios, across varying weather conditions and differing occlusion levels, underscores its applicability in practical deployments. Furthermore, annotation accuracy was rigorously assessed to ensure annotations upheld high-quality standards, thereby supporting reliable benchmarking outcomes.

The paper posits that more exhaustive datasets can address the remaining performance gaps between human-level perception and computer algorithms. With urban scenes offering diverse challenges—such as dense traffic, dynamic weather, and variable lighting conditions—EuroCity Persons provides a crucial resource for advancing object detection methodologies, especially in the pursuit of enhancing autonomous vehicle safety systems.

In conclusion, the EuroCity Persons dataset is a critical tool in advancing the frontier of object detection technologies in urban environments. Its extensive size and diversity present new opportunities for developing models that not only achieve higher accuracy but also demonstrate superior generalization across different geographies. As researchers push the boundaries of AI, datasets like EuroCity Persons will be instrumental in bridging the gap toward fully autonomous systems. Future exploration will likely focus on leveraging this dataset to refine joint detection and pose estimation techniques, contributing to the evolving landscape of AI in real-world applications.