Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

We study active object tracking, where a tracker takes visual observations (ie, frame
sequences) as input and produces the corresponding camera control signals as output (eg …

We study active object tracking, where a tracker takes as input the visual observation (ie
frame sequence) and produces the camera control signal (eg, move forward, turn left, etc) …

In active object tracking, given a visual observation as input, the goal is to lockup the target
by autonomously adjusting camera's position and posture. Previous works on active tracking …

Visual Active Tracking (VAT) aims at following a target object by autonomously controlling
the motion system of a tracker given visual observations. To learn a robust tracker for VAT, in …

Visual Active Tracking (VAT) aims at following a target object by autonomously controlling
the motion system of a tracker given visual observations. Previous work has shown that the …

Abstract Active Multi-Object Tracking (AMOT) is a task where cameras are controlled by a
centralized system to adjust their poses automatically and collaboratively so as to maximize …

In this work, we are dedicated to multi-target active object tracking (AOT), where the goal is
to achieve continuous tracking of targets through real-time control of camera. This form of …

Camera-based systems are frequently used to track moving objects which are in the field of
their view. This paper describes design and development of a camera-based tracking robot …

カメラを使った遠隔監視においては, 環境を常に広範囲に撮影し, かつ注目対象の実時間での検出・
追跡が求められる. 環境を常に広範囲に撮影するために回転カメラを用いる方法や全方位画像 …