In order to recognize people across long distances and from elevated viewpoints, biometric systems must handle the challenges of imaging through atmospheric turbulence and non-frontal presentations, in addition to the traditional A-PIE challenges of aging, pose, illumination, and expression. While individual biometric modalities such as facial appearance, gait, and whole body appearance each have a role to play, no single modality can address all of these challenges. This paper describes a novel multi-modal biometric recognition system that addresses the challenges of atmospheric turbulence, occlusions, and elevated viewpoints by combining these modalities. We demonstrate our system on both $R G B$ video-based identity verification and both open and closed-world search.

In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild. To our knowledge, MEVID represents the most-varied video person ReID dataset, spanning an extensive indoor and outdoor environment across nine unique dates in a 73-day window, various camera viewpoints, and entity clothing changes. Specifically, we label the identities of 158 unique people wearing 598 outfits taken from 8, 092 tracklets, average length of about 590 frames, seen in 33 camera views from the very-large-scale MEVA person activities dataset. While other datasets have more unique identities, MEVID emphasizes a richer set of information about each individual, such as: 4 outfits/identity vs. 2 outfits/identity in CCVID, 33 viewpoints across 17 locations vs. 6 in 5 simulated locations for MTA, and 10 million frames vs. 3 million for LS-VID. Being based on the MEVA video dataset, we also inherit data that is intentionally demographically balanced to the continental United States. To accelerate the annotation process, we developed a semi-automatic annotation framework and GUI that combines state-of-the-art real-time models for object detection, pose estimation, person ReID, and multi-object tracking. We evaluate several state-of-the-art methods on MEVID challenge problems and comprehensively quantify their robustness in terms of changes of outfit, scale, and background location. Our quantitative analysis on the realistic, unique aspects of MEVID shows that there are significant remaining challenges in video person ReID and indicates important directions for future research.

We present the Multiview Extended Video with Activities (MEVA) dataset[6], a new and very-large-scale dataset for human activity recognition. Existing security datasets either focus on activity counts by aggregating public video disseminated due to its content, which typically excludes same-scene background video, or they achieve persistence by observing public areas and thus cannot control for activity content. Our dataset is over 9300 hours of untrimmed, continuous video, scripted to include diverse, simultaneous activities, along with spontaneous background activity. We have annotated 144 hours for 37 activity types, marking bounding boxes of actors and props. Our collection observed approximately 100 actors performing scripted scenarios and spontaneous background activity over a three- week period at access-controled venue, collecting in multiple modalities with overlapping and non-overlapping in-door and outdoor viewpoints. The resulting data includes video from 38 RGB and thermal IR cameras, 42 hours of UAV footage, as well as GPS locations for the actors. 122 hours of annotation are sequestered in support of the NIST Activity in Extended Video (ActEV) challenge; the other 22 hours of annotation and the corresponding video are available on our website, along with an additional 306 hours of ground camera data, 4.6 hours of UAV data, and 9.6 hours of GPS logs. Additional derived data includes camera models geo-registering the outdoor cameras and a dense 3D point cloud model of the outdoor scene. The data was collected with IRB oversight and approval and released under a CC-BY-4.0 license.

We describe a system for content-based retrieval from large surveillance video archives, using behavior, action and appearance of objects. Objects are detected, tracked, and classified into broad categories. Their behavior and appearance are characterized by action detectors and descriptors, which are indexed in an archive. Queries can be posed as video exemplars, and the results can be refined through relevance feedback. The contributions of our system include the fusion of behavior and action detectors with appearance for matching; the improvement of query results through interactive query refinement (IQR), which learns a discriminative classifier online based on user feedback; and reasonable performance on low resolution, poor quality video. The system operates on video from ground cameras and aerial platforms, both RGB and IR. Performance is evaluated on publicly-available surveillance datasets, showing that subtle actions can be detected under difficult conditions, with reasonable improvement from IQR.

We address the problem of robot participation as a team member in group activities. In this human-robot interactive setting, the goal is to enable a robot to detect objects around it using on-board video cameras, and determine which activity they are performing. It then determines how it should move to participate in the activity based on its intended role, derived from domain knowledge, and human instruction. A human controller can initialize, correct and refine the robot's behavior and recognition of surrounding activities. A probabilistic framework is used for joint estimation of the robot's desired motion and the activity surrounding it while incorporating human instruction. Group activities consisting of multiple people moving about the robot with a common goal are used to demonstrate the proposed approach. We focus on activities with geometric structures (line, wedge, echelon, etc.) with an arbitrary number of participants, and handle the noisy observations from video detection and tracking. Results using both simulated and real video data are used to demonstrate activity recognition and incorporating human instruction.

We present a complete set of perceptual observables that provides a unified image description for grouping, figure-ground separation, and texture analysis. Although much progress has been made recently in treating contours and texture simultaneously for image segmentation and grouping, current approaches rely on different models for contours, regions, and texture such as one-dimensional intensity discontinuities for contours and filter bank responses for texture. This results in expensive computation that arbitrates between these disparate representations at each pixel. In our approach, salient image content such as contours, regions, and texture are represented in a common, low-level framework of image observables. We model the image as a partition of surfaces bounded by intensity discontinuities and derive perceptual measures as relations between neighboring surfaces. This enables us to extend the traditional Gestalt measures based on local edge geometry and contrast to region-based measures that jointly exploit large scale image topology, photometry, and geometry. These measures provide a natural basis for grouping on multidimensional similarity criteria and texture is directly derived as relational properties on local region neighborhoods. The viability of our model is demonstrated by applying the common observables to texture recognition, figure-ground separation, and generic image segmentation.