Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.Comment: arXiv admin note: text overlap with arXiv:1708.0361

Information theoretic sensor management for multi-target tracking with a single pan-tilt-zoom camera

Automatic multiple target tracking with pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the lit-erature, most of them proposing simplistic scenarios. In this paper, we present a PTZ camera management framework which lies on information theoretic principles: at each time step, the next camera pose (pan, tilt, focal length) is chosen, according to a policy which ensures maximum information gain. The formulation takes into account occlusions, phys-ical extension of targets, realistic pedestrian detectors and the mechanical constraints of the camera. Convincing com-parative results on synthetic data, realistic simulations and the implementation on a real video surveillance camera val-idate the effectiveness of the proposed method. 1

Non-myopic information theoretic sensor management of a single pan\u2013tilt\u2013zoom camera for multiple object detection and tracking

Detailed derivation of an information theoretic framework for real PTZ management.Introduction and implementation of a non-myopic strategy.Large experimental validation, with synthetic and realistic datasets.Working demonstration of myopic strategy on an off-the-shelf PTZ camera. Automatic multiple object tracking with a single pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the literature, most of them proposing simplistic scenarios. In this paper, we present a novel PTZ camera management framework in which at each time step, the next camera pose (pan, tilt, focal length) is chosen to support multiple object tracking. The policy can be myopic or non-myopic, where the former analyzes exclusively the current frame for deciding the next camera pose, while the latter takes into account plausible future target displacements and camera poses, through a multiple look-ahead optimization. In both cases, occlusions, a variable number of subjects and genuine pedestrian detectors are taken into account, for the first time in the literature. Convincing comparative results on synthetic data, realistic simulations and real trials validate our proposal, showing that non-myopic strategies are particularly suited for a PTZ camera management

Towards On-Line Saccade Planning for High-Resolution Image Sensing

This paper considers the problem of designing an active observer to plan a sequence of decisions regarding what target to look at, through a foveal-sensing action. We propose a framework in which a pan/tilt/zoom (PTZ) camera schedules saccades in order to acquire high resolution images (at least one) of as many moving targets as possible before they leave the scene. An intelligent choice of the order of sensing the targets can significantly reduce the total dead-time wasted by the active camera and, consequently, its cycle time. The grabbed images provide meaningful identification imagery of distant targets which are not recognizable in a wide angle view. We cast the whole problem as a particular kind of dynamic discrete optimization. In particular, we will show that the problem can be solved by modelling the attentional gaze control as a novel on-line Dynamic Vehicle Routing Problem (DVRP) with deadlines. Moreover we also show how multi-view geometry can be used for evaluating the cost of high resolution image sensing with a PTZ camera. Congestion analysis experiments are reported proving the effectiveness of the solution in acquiring high resolution images of a large number of moving targets in a wide area. The evaluation was conducted with a simulation using a dual camera system in a master-slave configuration. Camera performances are also empirically tested in order to validate how the manufacturer’s specification deviates from our model using an off-the-shelf PTZ camera