Towards using count-level weak supervision for crowd counting. (January 2021)

• Record Type: Journal Article
• Authors: Lei, Yinjie; Liu, Yan; Zhang, Pingping; Liu, Lingqiao
• Is Part Of: Pattern recognition. Volume 109(2021)
• Publication Date: 2021-01
• Subjects: Crowd counting -- Count-level annotation -- Weak supervision -- Auxiliary tasks learning -- Asymmetry training; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107616
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A count-level weak supervision framework is proposed in reducing the annotation cost for crowd counting. The Multiple Auxiliary Task Training (MATT) scheme is introduced to train a better weakly-supervised crowd counter. A newly introduced dataset, namely MSCC is designed to evaluate the weakly-supervised crowed counters. A superior performance than the straightforward weakly-supervised crowd counting method is achieved. Abstract: Most existing crowd counting methods require object location-level annotation which is labor-intensive and time-consuming to obtain. In contrast, weaker annotations that only label the total count of objects can be easy to obtain in many practical scenarios. This paper focuses on the problem of weakly-supervised crowd counting which learns a model from a small amount of location-level annotations (fully-supervised) and a large amount of count-level annotations (weakly-supervised). Our study reveals that the most straightforward, that is, directly regressing the integral of density map to the object count, fails to provide satisfactory performance. As an alternative solution, we propose a method by taking advantage of the fact that the total count can be estimated via different-but-equivalent density maps. Our key idea is to enforce the consistency between those density maps and total object count on weakly labeled images as regularization terms. We realize this idea by using multiple density map estimation branches and a carefully devised asymmetry training strategy, called Multiple Auxiliary Tasks Training (MATT). Through extensive experiments on existing datasets and a newly proposed dataset, we validate the effectiveness of the proposed weakly-supervised method and demonstrate its superior performance over existing solutions.