基于变形部件模型的密集场景多行人检测算法

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本文探讨了在密集场景下，基于变形部件模型的多行人检测方法对于视频监控的重要性。作者Lu Wang、Xiaoli Ji、Qing Xu和Mingxing Jia来自东北大学信息科学与工程学院，他们针对拥挤场景中行人检测的复杂性提出了创新性的解决方案。传统的行人检测任务在存在遮挡的情况下变得尤为困难，而他们的工作着重于解决这一问题。文章的核心贡献是将全身体检器分解为多个身体部位检测器，这样做可以提高检测效率。每个部位检测器的响应可以根据全身体检器的结果快速计算得出。作者不仅考虑了单个部位的检测得分，还纳入了不同部位检测之间的空间关系，通过这种方式生成假设。这种综合的方法允许系统更准确地识别在密集环境中可能出现的重叠或遮挡的行人。具体来说，流程包括以下几个步骤： 1. **部分分解**：将全身体检器拆分为若干个可独立处理的身体部位检测器，如头部、躯干和四肢。这样可以更好地应对局部遮挡，因为即使某个部位被遮挡，其他部位可能仍然可见。 2. **响应计算**：利用全身体检器的输出来高效地推断各个部位的检测结果，这有助于在短时间内进行大规模处理。 3. **联合分析**：基于检测得分和部位之间位置关系的考虑，形成多个可能的行人组合，这些组合可以减少误报并提高整体检测精度。 4. **局部优化**：最后，通过一种局部优化策略，对生成的假设进行细化和调整，以进一步提升在密集环境中的行人检测性能。这项研究提出了一种创新的行人检测框架，结合了变形部件模型的优势，并有效地解决了拥挤场景下的行人检测难题。这对于视频监控系统的实时性和准确性有着显著的提升，具有重要的实际应用价值。关键词包括变形部件模型（DeformablePart-basedModel）、多行人检测（MultiplePedestrianDetection）、拥挤人群检测（CrowdDetection）以及视频监控技术（VideoSurveillance）。

Deformable Part Model based Multiple Pedestrian Detection for

Video Surveillance in Crowded Scenes

Lu Wang, Xiaoli Ji, Qingxu Deng and Mingxing Jia

College of Information Science and Engineering, Northeastern University, Shenyang, China

{wanglu, jixiaoli, dengqingxu, jiamingxing}@ise.neu.edu.cn

Keywords: Deformable Part-based Model, Multiple Pedestrian Detection, Crowd Detection, Video Surveillance.

Abstract: Pedestrian detection is a challenging task for video surveillance. The problem becomes more difficult when

occlusion is prevalent. In this paper, we extend a deformable part-based pedestrian detector to pedestrian de-

tection in crowded scenes by considering both body part detection responses and detections' mutual spatial

relationship. Specifically, we first decompose the full body detector into several body part detectors, whose

detection responses can be computed efficiently from the response of the full body detector. Then, given the

detection responses of the body part detectors, hypotheses are nominated by considering both detection

scores and responses’ mutual spatial relationship. Finally, a local optimization process is applied to make

the final decision, where an objective function encouraging detections with high confidence, high discrimi-

nability and low conflict with other detections is proposed to select the best candidate detections. Experi-

mental results show the effectiveness of the proposed approach.

1 INTRODUCTION

Pedestrian detection is a very important task for

video surveillance. It is difficult due to pose articula-

tions, appearance variations, low figure-ground con-

trast and etc. Recently, significant advance has been

made on detecting well separated individual pedes-

trians through training detectors using statistical

machine learning methods and running the detectors

on the detection window that slides over image posi-

tions and across scale levels (Dollar, 2012). Howev-

er, when applied to the detection of crowds, their

performance degrades significantly due to ambigu-

ous appearance caused by heavy occlusions.

The deformable part-based model (DPM) trained

using latent support vector machine (Felzenszwalb,

2010) has been proved to be one of the most power-

ful object detectors. It runs detection on individual

parts and then sum up the responses to form the final

detection score. DPM has a good potential to apply

to crowd detection because parts can be flexibly

removed from and added to the model to deal with

occlusion. There are some works that apply the

DPM models to deal with occlusion (Ouyang, 2012);

(Shu, 2012); (Yan, 2012). However, (Ouyang, 2012)

and (Shu, 2012) focus on improving the responses in

a detection window without considering detection

responses of neighboring windows; only Yan, 2012

determines the visibility of part by simultaneously

considering the appearance and mutual spatial rela-

tionship. Therefore, the aim of this work is to adapt

a DPM based full body pedestrian detector to crowd

detection in surveillance scenarios by considering

both body part detection responses and detections'

mutual spatial relationship.

In this paper, we assume the camera looks down

onto a ground plane and no camera parameter is

known. Specifically, we first propose to decompose

the original whole body detector trained on the

INRIA pedestrian dataset into several body part

detectors, whose responses are computed efficiently,

and the bias term for each part detector is estimated

from the training data so that the same threshold can

be used to select responses from different body part

detectors. Then, given the detection responses of the

body part detectors, hypotheses that may correspond

to genuine pedestrians are nominated by considering

both detection scores and responses’ mutual spatial

relationship. Finally, a local optimization process is

applied to make the final decision, where an objec-

tive function encouraging detections with high con-

fidence, high discriminability and low conflict with

other detections is proposed to select the best detec-

tions from the mutually overlapped hypotheses.

599

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