One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches have enabled great advances in using camera imagery to detect and classify objects. But for a safety critical application, such as autonomous driving, the error rates of the current state of the art are still too high to enable safe operation. Moreover, the characterization of object detector performance is primarily limited to testing on prerecorded datasets. Errors that occur on novel data go undetected without additional human labels. In this letter, we propose an automated method to identify mistakes made by object detectors without ground truth labels . We show that inconsistencies in the object detector output between a pair of similar images can be used as hypotheses for false negatives (e.g., missed detections) and using a novel set of features for each hypothesis, an off-the-shelf binary classifier can be used to find valid errors. In particular, we study two distinct cues– temporal and stereo inconsistencies—using data that are readily available on most autonomous vehicles. Our method can be used with any camera-based object detector and we illustrate the technique on several sets of real world data. We show that a state-of-the-art detector, tracker, and our classifier trained only on synthetic data can identify valid errors on KITTI tracking dataset with an average precision of 0.94. We also release a new tracking dataset with 104 sequences totaling 80,655 labeled pairs of stereo images along with ground truth disparity from a game engine to facilitate further research.
Sensor fusion poses a related problem by using multiple modalities to understand the world and in some cases identify errors or provide self-supervised labeling @cite_1 . However, in this work we focus on identifying errors within camera imagery alone. This enables single frame object detectors to be independently validated and improved without the confounds of other modalities.
@cite_1
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A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single classifier. In the latter, two classifiers are employed, one per sensor-feature space, which were offline selected based on information theory and fused by a trainable fusion method applied over the likelihoods provided by the component classifiers. The proposed schemes for sensor combination, and more specifically the trainable fusion method, lead to enhanced detection performance and, in addition, maintenance of false-alarms under tolerable values in comparison with single-based classifiers. Experimental results highlight the performance and effectiveness of the proposed pedestrian detection system and the related sensor data combination strategies. © 2009 Wiley Periodicals, Inc.