Tattoos have been increasingly used as a discriminative soft biometric for people identification, such as criminal and victim identification in forensics investigation and law enforcement. However, automatic detection of tattoo images and accurate localization of the regions of interest are challenged by the large variations in artistic composition, color, shape, texture, location on the body, local geometric shape (e.g. neck and finger), imaging conditions, and image quality. In this paper, we train a tattoo detector from the Tatt-C and PASCAL VOC 2007 image datasets using region-based deep learning. The detector can effectively determine if an image contains tattoos and the locations of tattoo regions. We carry out a comprehensive evaluation of our tattoo image classification and detection localization. The detector improves upon the state-of-the-art algorithms in the Tatt-C challenge, achieving a better detection error trade-off curve. It yields low confidence scores on randomly sampled non-tattoo images from 397 scene categories in the MIT-SUN dataset. In addition, the same detector is also validated on the NTU Tattoo Image Dataset with 10000 images.

We present a real-time, full-frame, multi-target Wide Area Motion Imagery (WAMI) tracking system that utilizes distributed processing to handle high data rates while maintaining high track quality. The proposed architecture processes the WAMI data as a series of geospatial tiles and implements both process- and thread-level parallelism across multiple compute nodes. Each tile is processed independently, from decoding the image through generating tracks that are finally merged across all tiles by an inter-tile linker (ITL) module. A high performance PostgreSQL database with GIS extensions is used to control the flow of intermediate data between each tracking process. High quality tracks are produced efficiently due to robust, effective algorithmic modules including: multi-frame moving object detection and track initialization; tracking based on the fusion of motion and appearance with a goal of very pure tracks; and online track linking based on multiple features. In addition, we have configured a high-performance compute cluster using high density blade servers, Infiniband networking, and an HPC filesystem. The compute cluster enables full-frame, state-of-the-art tracking of vehicles or dismounts at the WAMI sensor's native 1.25Hz frame-rate, while only taking 7u of rack space and providing 210 megapixels/second throughput.

Illumination changes are a ubiquitous problem in computer vision. They present a challenge in many applications, including tracking: for example, an object may move in and out of a shadow. We present a new tracking algorithm which is insensitive to illumination changes, while at the same time using all of the available photometric information. The algorithm is based on computing an illumination-invariant optical flow field; the computation is made robust by using a graph cuts formulation. Experimentally, the new technique is shown to quite reliable in both synthetic and real sequences, dealing with a variety of illumination changes that cause problems for density based trackers.