Novel dataset creation for all multi-object tracking, crowd-counting, and
industrial-based videos is arduous and time-consuming when faced with a unique
class that densely populates a video sequence. We propose a time efficient
method called POPCat that exploits the multi-target and temporal features of
video data to produce a semi-supervised pipeline for segmentation or box-based
video annotation. The method retains the accuracy level associated with human
level annotation while generating a large volume of semi-supervised annotations
for greater generalization. The method capitalizes on temporal features through
the use of a particle tracker to expand the domain of human-provided target
points. This is done through the use of a particle tracker to reassociate the
initial points to a set of images that follow the labeled frame. A YOLO model
is then trained with this generated data, and then rapidly infers on the target
video. Evaluations are conducted on GMOT-40, AnimalTrack, and Visdrone-2019
benchmarks. These multi-target video tracking/detection sets contain multiple
similar-looking targets, camera movements, and other features that would
commonly be seen in "wild" situations. We specifically choose these difficult
datasets to demonstrate the efficacy of the pipeline and for comparison
purposes. The method applied on GMOT-40, AnimalTrack, and Visdrone shows a
margin of improvement on recall/mAP50/mAP over the best results by a value of
24.5%/9.6%/4.8%, -/43.1%/27.8%, and 7.5%/9.4%/7.5% where metrics were
collected.

通过利用视频数据的多目标和时间特征，我们提出了一种名为 POPCat 的高效方法来创建用于分割或基于框的视频注释的半监督管道，使其在产生更大泛化能力的半监督注释的同时保持与人工注释相当的准确性。该方法通过使用粒子追踪器利用时间特征来扩展人工提供的目标点的域，然后利用此生成的数据训练 YOLO 模型，并快速推断目标视频。对 GMOT-40、AnimalTrack 和 Visdrone-2019 基准进行评估，这些多目标视频跟踪 / 检测集包含多个外观相似的目标、摄像机运动和其他在 “野外” 情况中常见的特征。我们特别选择这些困难的数据集来展示该管道的有效性并进行比较，结果显示在 GMOT-40、AnimalTrack 和 Visdrone 上相对于最佳结果的召回率 /mAP50/mAP 上分别提高了 24.5%/9.6%/4.8%、-/43.1%/27.8% 和 7.5%/9.4%/7.5%。

POPCat: 复杂注释任务的粒子传播

POPCat: Propagation of particles for complex annotation tasks

State-of-the-art methods for counting people in crowded scenes rely on deep
networks to estimate crowd density. While effective, deep learning approaches
are vulnerable to adversarial attacks, which, in a crowd-counting context, can
lead to serious security issues. However, attack and defense mechanisms have
been virtually unexplored in regression tasks, let alone for crowd density
estimation.
In this paper, we investigate the effectiveness of existing attack strategies
on crowd-counting networks, and introduce a simple yet effective pixel-wise
detection mechanism. It builds on the intuition that, when attacking a
multitask network, in our case estimating crowd density and scene depth, both
outputs will be perturbed, and thus the second one can be used for detection
purposes. We will demonstrate that this significantly outperforms heuristic and
uncertainty-based strategies.

本文旨在探讨已有的网络攻防机制在人群计数任务上的有效性。我们提出了一种简单而有效的像素检测机制，利用一个多任务网络中各个输出的相互作用，充分发挥了深度网络在人群计数任务中的性能。最终结果表明我们的机制在攻击与防御之间实现了较好的平衡。