Gaze- and Spacing-flow Unveil Intentions: Hidden Follower Discovery

Xu, Danni; Hu, Ruimin; Wang, Zheng; Luo, Linbo; Li, Dengshi; Zeng, Wenjun

doi:10.1145/3503161.3548207

Cited by 2 publications

(10 citation statements)

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“…Gaze-Spacing Flow. Xu et al (2022) define the gazespacing pattern to represent behavioral differences between the hidden follower F and the target T :…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

“…In the time domain, Xu et al (2022) represents the spacing pattern of the distance series using the spacing flow features: distance range and average distance; and represents the gaze pattern of the gaze state series using the gaze flow features: gaze frequency and gaze density.…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

“…Gaze State Series and Distance Series. In HFDF-GS (Xu et al 2022), the gaze state series only uses a coarse-grained representation (1 or 0) of threshold dichotomy to determine gaze or not, which is not enough to describe complex gaze behavior. In this paper, we introduce a dynamic score to represent the gaze degree at timestep i.…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

“…The study of "hidden" behavior has seen new light in recent works (Xu et al 2022(Xu et al , 2021. They consider behavior patterns can reflect human intentions: first found there are significant pattern differences in gaze-spatial behavior between hidden followers and normal pedestrians.…”

Section: Introductionmentioning

confidence: 99%

“…Based on frequency-domain analysis, we then propose a decision-fusion hidden follower detection framework based on reversible time-frequency transform (HFDF-TF). HFDF-TF detects the gaze direction and trajectory for all pedestrians in the surveillance to generate the gaze state series and distance series (Xu et al 2022), and then our framework acts on it to mine the frequency-domain features that distinguish hidden followers from location followers. The contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

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Hidden Follower Detection: How Is the Gaze-Spacing Pattern Embodied in Frequency Domain?

Li,

Hu,

et al. 2024

AAAI

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Spatiotemporal social behavior analysis is a technique that studies the social behavior patterns of objects and estimates their risks based on their trajectories. In social public scenarios such as train stations, hidden following behavior has become one of the most challenging issues due to its probability of evolving into violent events, which is more than 25%. In recent years, research on hidden following detection (HFD) has focused on differences in time series between hidden followers and normal pedestrians under two temporal characteristics: gaze and spatial distance. However, the time-domain representation for time series is irreversible and usually causes the loss of critical information. In this paper, we deeply study the expression efficiency of time/frequency domain features of time series, by exploring the recovery mechanism of features to source time series, we establish a fidelity estimation method for feature expression and a selection model for frequency-domain features based on the signal-to-distortion ratio (SDR). Experimental results demonstrate the feature fidelity of time series and HFD performance are positively correlated, and the fidelity of frequency-domain features and HFD performance are significantly better than the time-domain features. On both real and simulated datasets, the accuracy of the proposed method is increased by 3%, and the gaze-only module is improved by 10%. Related research has explored new methods for optimal feature selection based on fidelity, new patterns for efficient feature expression of hidden following behavior, and the mechanism of multimodal collaborative identification.

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“…Gaze-Spacing Flow. Xu et al (2022) define the gazespacing pattern to represent behavioral differences between the hidden follower F and the target T :…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

Section: Definitions and Preliminariesmentioning

confidence: 99%

Section: Definitions and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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