The performance of optical flow algorithms greatly depends on the specifics
of the content and the application for which it is used. Existing and well
established optical flow datasets are limited to rather particular contents
from which none is close to crowd behavior analysis; whereas such applications
heavily utilize optical flow. We introduce a new optical flow dataset
exploiting the possibilities of a recent video engine to generate sequences
with ground-truth optical flow for large crowds in different scenarios. We
break with the development of the last decade of introducing ever increasing
displacements to pose new difficulties. Instead we focus on real-world
surveillance scenarios where numerous small, partly independent, non rigidly
moving objects observed over a long temporal range pose a challenge. By
evaluating different optical flow algorithms, we find that results of
established datasets can not be transferred to these new challenges. In
exhaustive experiments we are able to provide new insight into optical flow for
crowd analysis. Finally, the results have been validated on the real-world UCF
crowd tracking benchmark while achieving competitive results compared to more
sophisticated state-of-the-art crowd tracking approaches.

介绍了一种新的光流数据集，用于解决人群行为分析领域中光流性能测试数据不足的问题，并提供了实验结果表明现有的光流算法不能有效地适应这种人群的运动及复杂性，该数据集在实际应用中表现出较高的性能。

用于视觉人群分析的光流数据集和基准

Optical Flow Dataset and Benchmark for Visual Crowd Analysis

Multi-object tracking has been studied for decades. However, when it comes to
tracking pedestrians in extremely crowded scenes, we are limited to only few
works. This is an important problem which gives rise to several challenges.
Pre-trained object detectors fail to localize targets in crowded sequences.
This consequently limits the use of data-association based multi-target
tracking methods which rely on the outcome of an object detector. Additionally,
the small apparent target size makes it challenging to extract features to
discriminate targets from their surroundings. Finally, the large number of
targets greatly increases computational complexity which in turn makes it hard
to extend existing multi-target tracking approaches to high-density crowd
scenarios. In this paper, we propose a tracker that addresses the
aforementioned problems and is capable of tracking hundreds of people
efficiently. We formulate online crowd tracking as Binary Quadratic Programing.
Our formulation employs target's individual information in the form of
appearance and motion as well as contextual cues in the form of neighborhood
motion, spatial proximity and grouping constraints, and solves detection and
data association simultaneously. In order to solve the proposed quadratic
optimization efficiently, where state-of art commercial quadratic programing
solvers fail to find the answer in a reasonable amount of time, we propose to
use the most recent version of the Modified Frank Wolfe algorithm, which takes
advantage of SWAP-steps to speed up the optimization. We show that the proposed
formulation can track hundreds of targets efficiently and improves state-of-art
results by significant margins on eleven challenging high density crowd
sequences.

本文提出一种针对高密度人群场景的多目标追踪算法，采用二次规划方法和 Modified Frank Wolfe 算法，同时利用外观、运动、邻域运动、空间邻近性和分组约束等上下文信息进行探测和数据关联，有效提升目标检测效率，并在 11 个高密度人群序列上实验证明其较现有方法有显著的优势。