We present a new and complex traffic dataset, METEOR, which captures traffic patterns in unstructured scenarios in India. METEOR consists of more than 1000 one-minute video clips, over 2 million annotated frames with ego-vehicle trajectories, and more than 13 million bounding boxes for surrounding vehicles or traffic agents. METEOR is a unique dataset in terms of capturing the heterogeneity of microscopic and macroscopic traffic characteristics. Furthermore, we provide annotations for rare and interesting driving behaviors such as cut-ins, yielding, overtaking, overspeeding, zigzagging, sudden lane changing, running traffic signals, driving in the wrong lanes, taking wrong turns, lack of right-of-way rules at intersections, etc. We also present diverse traffic scenarios corresponding to rainy weather, nighttime driving, driving in rural areas with unmarked roads, and high-density traffic scenarios. We use our novel dataset to evaluate the performance of object detection and behavior prediction algorithms. We show that state-of-the-art object detectors fail in these challenging conditions and also propose a new benchmark test: action-behavior prediction with a baseline mAP score of 70.74.
翻译:我们提出了一个新的复杂的交通数据集,即METEOR,它捕捉印度非结构化情景中的交通模式。METEOR由超过1000个一分钟的视频剪辑、超过200万个带有自我车辆轨迹的附加说明的框和超过1 300万个环绕车辆或交通代理物的捆绑箱组成。METEOR是一个独特的数据集,用于捕捉微型和宏观交通特征的异质性。此外,我们提供说明,说明稀有和有趣的驾驶行为,如切入、产生、超载、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、超速、运行交通信号、运行交通信号、在错误的航道中驾驶、走错转、交叉点缺乏右路规则等。我们还介绍了与雨季、夜间驾驶、农村地区驾驶无标记道路和高密度交通情景相关的多种交通情况。我们使用我们的新数据集来评价物体探测和行为预测算法的性7074等。我们显示,在这些具有挑战性条件下的状态的物体探测器探测器探测器失灵,并提出了新的基准预测。我们还提议进行新的基准测试。