TY  - JOUR
PY  - 2021//
TI  - Deep multi-level feature pyramids: application for non-canonical firearm detection in video surveillance
JO  - Engineering applications of artificial intelligence
A1  - Lim, JunYi
A1  - Al Jobayer, Md Istiaque
A1  - Baskaran, Vishnu Monn
A1  - Lim, Joanne MunYee
A1  - See, John
A1  - Wong, KokSheik
SP  - e104094
EP  - e104094
VL  - 97
IS  - 
N2  - The epidemic of gun violence worldwide necessitates the need for an active-based video surveillance network to combat this crime. In this context, autonomously detecting handguns is crucial in capturing firearm-related crimes. However, current object detectors using deep learning are unable to capture handguns at different scales in an unconstrained environment. Hence, this paper puts forward an enhanced deep multi-level feature pyramid network that addresses the difficulty in inferring handguns from a non-canonical perspective. We first construct a dataset containing handguns in an unconstrained environment for representation learning. The dataset is constructed from a set of 250 recorded videos and with over 2500 distinct labeled frames. Crucially, these labeled frames account for the absence of a proper video surveillance-based handgun dataset. We then train the dataset on a multi-level multi-scale object detector, i.e., M2Det. We further improve the performance of M2Det by: (1) Enhancing the base features by concatenating shallow, medium and deep features from the backbone according to its relative receptive field; (2) Implementing generalized intersection-over-union as its localization loss; and (3) Integrating Focal Loss as its classification loss to improve detection of small-scale handguns. Experiments on a challenging video surveillance test dataset demonstrate that the proposed model achieves 87.42% accuracy. In addition, we implement adaptive surveillance image partitioning to redetect handguns at specific regions. This method potentially solves the challenge of sporadically poor real-world handgun classifications. This model is capable of pioneering non-canonical handgun detection for active-based video surveillance systems. The dataset and trained models are available at: https://github.com/MarcusLimJunYi/Monash-Guns-Dataset.<p />  <p>Language: en</p>
LA  - en
SN  - 0952-1976
UR  - http://dx.doi.org/10.1016/j.engappai.2020.104094
ID  - ref1
ER  -