A Computer Vision Based Approach for Stalking Detection Using a
  CNN-LSTM-MLP Hybrid Fusion Model
Murad Hasan Shahriar Iqbal Md. Billal Hossain Faisal Md. Musnad Hossin Neloy Md. Tonmoy Kabir Md. Tanzim Reza Md. Golam Rabiul Alam Md Zia Uddin
Criminal and suspicious activity detection has become a popular research
topic in recent years. The rapid growth of computer vision technologies has had
a crucial impact on solving this issue. However, physical stalking detection is
still a less explored area despite the evolution of modern technology.
Nowadays, stalking in public places has become a common occurrence with women
being the most affected. Stalking is a visible action that usually occurs
before any criminal activity begins as the stalker begins to follow, loiter,
and stare at the victim before committing any criminal activity such as
assault, kidnapping, rape, and so on. Therefore, it has become a necessity to
detect stalking as all of these criminal activities can be stopped in the first
place through stalking detection. In this research, we propose a novel deep
learning-based hybrid fusion model to detect potential stalkers from a single
video with a minimal number of frames. We extract multiple relevant features,
such as facial landmarks, head pose estimation, and relative distance, as
numerical values from video frames. This data is fed into a multilayer
perceptron (MLP) to perform a classification task between a stalking and a
non-stalking scenario. Simultaneously, the video frames are fed into a
combination of convolutional and LSTM models to extract the spatio-temporal
features. We use a fusion of these numerical and spatio-temporal features to
build a classifier to detect stalking incidents. Additionally, we introduce a
dataset consisting of stalking and non-stalking videos gathered from various
feature films and television series, which is also used to train the model. The
experimental results show the efficiency and dynamism of our proposed stalker
detection system, achieving 89.58% testing accuracy with a significant
improvement as compared to the state-of-the-art approaches.