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\begin{document}
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\title{Deep Contextual Analysis for Enhanced Suspiciousness Estimation}



\author[1]{Dr. Kuldeep S Yadav}%
\author[1]{Lalan Kumar}%
\affil[1]{Affiliation not available}%


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  \date{April 29, 2026}


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\begin{abstract}
Suspiciousness detection is crucial for anticipating potential security
threats and facilitating timely intervention and risk mitigation.
Enhanced by deep learning, vision-based systems can play a significant
role in this area. This paper presents a computationally efficient
vision-based suspiciousness estimation system cascading detection,
classification, and analysis modules. It incorporates factors like
suspicious objects, facial expressions, and abnormal body language. A
suspicious object detector (SOD) is designed to precisely locate the
objects invariant to the size, scale, rotation, translation, and
occlusion. A deep convolutional neural network is implemented for body
language and facial expression recognition with the image and landmark
features as its input. To estimate the final suspiciousness, an
algorithm (USE-riskometer) is proposed in this work by considering all
the detected and analyzed factors. All the modules are separately
trained with the corresponding object detection and facial expression
datasets. In addition to this, a novel dataset for suspiciousness
estimation is proposed in this work. This dataset includes various
suspicious elements, such as weapons, fire, crowd presence, facial
expressions, and body language in an uncontrolled environment. Each
module, along with the dataset, is evaluated against state-of-the-art
methods, demonstrating robustness. This work represents a significant
advancement in preemptive security measures, leveraging advanced
technologies for improved recognition of suspicious activities.%
\end{abstract}%



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