Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking frameworks have two key limitations: 1) They are monolithic, proprietary, and lack the ability to rapidly incorporate sophisticated tracking models; and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera-network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance and the active camera-set size. We illustrate the concise expressiveness of the programming model for 4 tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance and quality trade-offs enabled by our dynamic tracking, batching and dropping strategies.
Video surveillance system has become a critical part in the security and protection system of modem cities, since smart monitoring cameras equipped with intelligent video analytics techniques can monitor and pre-alarm abnormal behaviors or events. However, with the expansion of the surveillance network, massive surveillance video data poses huge challenges to the analytics, storage and retrieval in the Big Data era. This paper presents a novel intelligent processing and utilization solution to big surveillance video data based on the event detection and alarming messages from front-end smart cameras. The method includes three parts: the intelligent pre-alarming for abnormal events, smart storage for surveillance video and rapid retrieval for evidence videos, which fully explores the temporal-spatial association analysis with respect to the abnormal events in different monitoring sites. Experimental results reveal that our proposed approach can reliably pre-alarm security risk events, substantially reduce storage space of recorded video and significantly speed up the evidence video retrieval associated with specific suspects. Despite significant interest in the research community, the development of multi-camera applications is still quite challenging. This paper presents Ella - a dedicated publish subscribe middleware system that facilitates distribution, component reuse and communication for heterogeneous multi-camera applications. We present the key components of this middleware system and demonstrate its applicability based on an autonomous multi-camera person tracking application. Ella is able to run on resource-limited and heterogeneous VSNs. We present performance measurements on different hardware platforms as well as operating systems.