ITUF dataset consists of images of Guns and Rifles from different scenarios of practical importance such as being pointed, being carried, lying on tables, ground or in racks. These variations allow machine learning algorithms to overcome dress variations, body pose variations, firearm pose and size variations, varying light conditions and both indoor & outdoor scenarios making a strong prior for data driven algorithms.

Orientation Aware Object Detection with Applications to Firearms
Javed Iqbal, Muhammad Akhtar Munir, Arif Mahmood, Afsheen Rafaqat Ali, Mohsen Ali

[Paper] [Project Website] [Code] [Download the dataset (Google Forms)]

We created our own synthetic dataset, called RBSF (Real Background, Synthetic foreground), by overlaying the 20 different foreground objects performing various movements with 5 different real background videos. We downloaded both the foregrounds and backgrounds videos from YouTube and mixed them using Video Editing Tool.

EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
Muhammad Faisal, Ijaz Akhter, Mohsen Ali, Richard Hartley

IEEE Winter Conference on Applications of Computer Vision(WACV-2020)

[Paper] [Project Website] [Code] [Download the dataset]

UCF-Crime dataset is a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities.

Real-world Anomaly Detection in Surveillance Videos
Waqas Sultani, Chen Chen, Mubarak Shah
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

[Paper] [Project Website] [Note] [Code] [Download the dataset]

Rwanda’s built-up region segmentation dataset is a realistic and challenging High-Resolution satellite imagery collection. The segmentation labels are created by hand-tagging the 73.4 sq-km of Rwanda, capturing a variety of build-up structures over different terrain. The developed dataset is spatially rich compared to existing datasets and covers diverse built-up scenarios including built-up areas in forests and deserts, mud houses, tin, and colored rooftops.

Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery
Javed Iqbal, Mohsen Ali
ISPRS Journal of Photogrammetry and Remote Sensing, 2020

[Paper] [Project Website] [Code] [Download the dataset]