IEEE Trans Image Process - Detection of dynamic background due to swaying movements from motion features.

Tópicos

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Resumo

Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.

Resumo Limpo

dynam chang background dynam background still present great challeng mani motionbas video surveil system context event detect major sourc fals alarm strong need secur industri either detect suppress fals alarm dampen effect background chang increas sensit meaning event interest paper restrict focus one common caus dynam background chang sway tree branch shadow windi condit consid ultim goal video analyt pipelin formul new dynam background detect problem signal process altern previous describ unreli comput visionbas approach within new framework direct reduc number fals alarm test detect event due characterist background motion addit introduc new data set suitabl evalu dynam background detect consist realworld event detect commerci surveil system two static surveil camera research question address whether dynam background can detect reliabl effici use simpl motion featur presenc similar meaning event loiter inspir tree aerodynam theori propos novel method name local variat persist lvp captur key characterist sway motion method pose convex optim problem whose variabl local variat deriv comput effici algorithm solv optim problem solut use form power detect statist newli collect data set demonstr propos lvp achiev excel detect result outperform best altern adapt exist art dynam background literatur

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