Bilattice-based Logical Reasoning for Human Detection

V. Shet, J. Neumann, Visvanathan Ramesh, L. Davis
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引用次数: 124

Abstract

The capacity to robustly detect humans in video is a critical component of automated visual surveillance systems. This paper describes a bilattice based logical reasoning approach that exploits contextual information and knowledge about interactions between humans, and augments it with the output of different low level detectors for human detection. Detections from low level parts-based detectors are treated as logical facts and used to reason explicitly about the presence or absence of humans in the scene. Positive and negative information from different sources, as well as uncertainties from detections and logical rules, are integrated within the bilattice framework. This approach also generates proofs or justifications for each hypothesis it proposes. These justifications (or lack thereof) are further employed by the system to explain and validate, or reject potential hypotheses. This allows the system to explicitly reason about complex interactions between humans and handle occlusions. These proofs are also available to the end user as an explanation of why the system thinks a particular hypothesis is actually a human. We employ a boosted cascade of gradient histograms based detector to detect individual body parts. We have applied this framework to analyze the presence of humans in static images from different datasets.
基于双栅格的人类检测逻辑推理
鲁棒检测视频中的人的能力是自动视觉监控系统的关键组成部分。本文描述了一种基于双边格的逻辑推理方法,该方法利用人类之间相互作用的上下文信息和知识,并通过不同低水平检测器的输出来增强它,用于人类检测。来自低级部件检测器的检测被视为逻辑事实,并用于明确地推断场景中是否存在人类。来自不同来源的正面和负面信息,以及来自检测和逻辑规则的不确定性,被整合在双边框架内。这种方法也为它提出的每个假设产生证明或证明。这些证明(或缺乏证明)被系统进一步用来解释和验证,或拒绝潜在的假设。这使得系统可以明确地推理人类之间复杂的相互作用并处理闭塞。最终用户也可以使用这些证明来解释为什么系统认为特定的假设实际上是人类。我们采用增强级联梯度直方图为基础的检测器来检测单个身体部位。我们已经应用这个框架来分析来自不同数据集的静态图像中人类的存在。
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