论文标题
多个对象跟踪从层次上的聚类轨迹从外观进行
Multiple Object Tracking from appearance by hierarchically clustering tracklets
论文作者
论文摘要
多个对象跟踪中的当前方法（MOT）依赖于检测之间的时空连贯性与对象外观相结合以匹配连续帧的对象。在这项工作中，我们使用对象出现作为视频中对象之间的关联的主要来源探索MOT，以空间和时间先验作为加权因素。我们通过利用以下想法来形成初始的曲目，即及时接近的对象的实例在外观上应该相似，并通过以层次结构方式融合曲目来构建最终对象轨道。我们进行了广泛的实验，这些实验表明我们的方法比三个不同的MOT基准Mot17，Mot20和Dancetrack在MOT17和MOT20中具有竞争力，并且在Dancetrack中建立最先进的结果。
Current approaches in Multiple Object Tracking (MOT) rely on the spatio-temporal coherence between detections combined with object appearance to match objects from consecutive frames. In this work, we explore MOT using object appearances as the main source of association between objects in a video, using spatial and temporal priors as weighting factors. We form initial tracklets by leveraging on the idea that instances of an object that are close in time should be similar in appearance, and build the final object tracks by fusing the tracklets in a hierarchical fashion. We conduct extensive experiments that show the effectiveness of our method over three different MOT benchmarks, MOT17, MOT20, and DanceTrack, being competitive in MOT17 and MOT20 and establishing state-of-the-art results in DanceTrack.
