  As camera networks have become more ubiquitous over the past decade, the
research interest in video management has shifted to analytics on multi-camera
networks. This includes performing tasks such as object detection, attribute
identification, and vehicle/person tracking across different cameras without
overlap. Current frameworks for management are designed for multi-camera
networks in a closed dataset environment where there is limited variability in
cameras and characteristics of the surveillance environment are well known.
Furthermore, current frameworks are designed for offline analytics with
guidance from human operators for forensic applications. This paper presents a
teamed classifier framework for video analytics in heterogeneous many-camera
networks with adversarial conditions such as multi-scale, multi-resolution
cameras capturing the environment with varying occlusion, blur, and
orientations. We describe an implementation for vehicle tracking and vehicle
re-identification (re-id), where we implement a zero-shot learning (ZSL) system
that performs automated tracking of all vehicles all the time. Our evaluations
on VeRi-776 and Cars196 show the teamed classifier framework is robust to
adversarial conditions, extensible to changing video characteristics such as
new vehicle types/brands and new cameras, and offers real-time performance
compared to current offline video analytics approaches.
