Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.

人群场景中的小组检测和跟踪是高级人群分析任务的基础。但是，它在生成适当的组和处理组配置的动态更改方面存在歧义。在本文中，我们提出了一种新颖的基于延迟决策的方法来解决上述问题，其动机是可以使用丰富的时间上下文来解决这些歧义。具体而言，给定单个检测，将生成小组假设。然后，将候选组假设跨连续帧及其潜在关联建立在树中。通过从假设树中寻找最佳的非冲突子集，可以确定小组并同时获得其轨迹。因此，此框架称为联合检测和跟踪。该联合框架通过展望未来的几个框架，减少了小组决策和跟踪中的歧义。但是，由于轨迹假设的数量随时间呈指数增长，因此导致解决方案空间无法控制。为了解决此问题，开发了有效的修剪策略，该策略可以使解决方案空间保持可管理状态，并且可以提高小组的信誉。在公共数据集上的实验证明了我们方法的有效性。即使在嘈杂的人群场景中，该方法也可以实现最先进的性能。这样可以使解决方案空间易于管理，并提高小组的信誉。在公共数据集上的实验证明了我们方法的有效性。即使在嘈杂的人群场景中，该方法也可以实现最先进的性能。这样可以保持解决方案空间的可管理性，并提高小组的信誉。在公共数据集上的实验证明了我们方法的有效性。即使在嘈杂的人群场景中，该方法也可以实现最先进的性能。