Jongwon Choi

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

This paper proposes a novel tracker which is controlled by sequentially pursuing actions learned by deep reinforcement learning. In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale. The deep network to control actions is pre-trained using various training sequences and fine-tuned during tracking for online adaptation to target and background changes. The pre-training is done by utilizing deep reinforcement learning as well as supervised learning. The use of reinforcement learning enables even partially labeled data to be successfully utilized for semi-supervised learning. Through evaluation of the OTB dataset, the proposed tracker is validated to achieve a competitive performance that is three times faster than state-of-the-art, deep network–based trackers. The fast version of the proposed method, which operates in real-time on GPU, outperforms the state-of-the-art real-time trackers.

Visual Tracking Using Attention-Modulated Disintegration and Integration

In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types. In the integration stage it recombines the units to memorize and recognize the target object effectively. With respect to the elementary trackers, we present a novel attentional feature-based correlation filter (AtCF) that focuses on distinctive attentional features. The effectiveness of the proposed algorithm is validated through experimental comparison with state-of-theart methods on widely-used tracking benchmark datasets.

Attentional Correlation Filter Network for Adaptive Visual Tracking

We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robustness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.

Photosensitivity of InZnO thin-film transistors using a solution process

Oxide semiconductor devices play a role in both switches and photo-sensors in interactive displays. During the fabrication of oxide semiconductor devices, the sol-gel solution process that is used to form an oxide semiconductor has various merits, including its simplicity and low cost as well as its good composition controllability. Here, we present the photosensitivity characteristics of an oxide photo thin-film transistor (TFT) created using the InZnO (IZO) sol-gel process. Upon exposure to light, photocurrent (Iphoto) in the negative gate bias regime is significantly increased with a negligible threshold voltage shift. The photosensitivity is modulated by geometrical factors and by the IZO material composition. We observed a significant effect of the channel thickness and IZO composition on the photosensitivity, which was attributed to the screening effect of optically ionized oxygen vacancies (Vo++). In particular, the optimized bi-layered oxide photo-TFT presents a good Iphoto/Idark photosensitivity ...

Robust pan-tilt-zoom tracking via optimization combining motion features and appearance correlations

This paper proposes a new pan-tilt-zoom (PTZ) tracking method to improve the robustness against occlusions and appearance changes by using motion likelihood map and scale change estimation as well as appearance correlation filter. For this purpose, we introduce a motion likelihood map constructed from motion detection result in addition to the correlation filter. The motion likelihood map is generated by blurring the motion detection result, which shows high probability in the center of target. To combine the correlation filter and the motion likelihood map, we formulate an optimization problem. In addition, to handle the scale change of target, we repeat the combining process for various scale of bounding box. The experiments show that the proposed method outperforms the state-of-the-art methods.

Patch-based fire detection with online outlier learning

Fire detection is one of the most interesting issues for surveillance. The existing approaches for the fire detection suffer from a high false positive ratio. To solve the problems, we present a patch-based fire detection algorithm with online outlier learning. In the proposed algorithm, the candidates of fire are obtained in the form of patch, while the classical candidates have been based on pixels or blobs. Because the patches of fire have more distinctive shape than the entire fire, the shape classifier can recognize the candidates correctly from fire-like outliers. In addition, we propose an online outlier learning scheme which handles the irregularity of fire based on the repeatability of shape in time. The proposed algorithm is experimented with new challenging dataset, consisting of 50 positive videos with fire and 44 negative ones with fire-like outliers. By evaluating on the dataset, we validate the performance of our algorithm qualitatively and quantitatively.

The Korean government and public policies in a development nexus

Introduction: The Role of Government in Koreas Economic and Social Transition.- Part I Government and Coordination for Development.- Institutional Presidency and National Development.- Managing Economic Policy and Coordination: A Saga of the Economic Planning Board.- Bureaucratic Power and Government Competitiveness.- Part II Public Policies for Development.- Governing the Developmental Welfare State: From Regulation to Provision.- Trade Policy for Development: Paradigm Shift from Mercantilism to Liberalism.- Educational Policy, Development of Education, and Economic Growth in Korea.

Density-Aware Pedestrian Proposal Networks for Robust People Detection in Crowded Scenes

In this paper, we propose a density-aware pedestrian proposal network (DAPPN) for robust people detection in crowded scenes. Conventional pedestrian detectors and object proposal algorithms easily fail to find people in crowded scenes because of severe occlusions among people. Our method utilizes a crowd density map to resolve the occlusion problem. The proposed network is composed of two networks: the proposal network and the selection network. First, the proposal network predicts the initial pedestrian detection proposals and the crowd density map. After that, the selection network selectively picks the final proposals by considering the initial proposals and the crowd density. To validate the performance of the proposed method, experiments are conducted on crowd-scene datasets: WorldExpo10 and PETS2009. The experimental results show that our method outperforms the conventional method and achieves near real-time speed on a GPU (25 fps).

User interactive segmentation with partially growing random forest

This paper proposes a novel approach for user interactive segmentation based on graph-cut, which improves the robustness against the initial parameter setting. The existing graph-cut based segmentation uses a parametric model to estimate the color distributions of foreground/background. However, the parametric model is sensitive to the predefined number of distribution models and can be easily biased by a wrong initialization. In this paper, we develop a non-parametric approach based on random forest to handle the biased initialization problem. In addition, we design a new structure of random forest referred to as partially growing random forest to reduce the training time. We compare the proposed approach quantitatively and qualitatively to the existing graph-cut based segmentation baseline, where our method shows a remarkable performance on the new colorful dataset as well as comparable results on the classical dataset.

Gradient preserving RGB-to-gray conversion using random forest.

This paper proposes a new algorithm for color-to-gray conversion preserving the gradient information in input color image. To preserve the gradient in a color image, we construct a random forest representing the relation between color intensity and gradient in an input image. The leaf nodes of random trees indicate the gray colors (single channel colors) corresponding to the input RGB colored pixels. From these initial gray colors obtained by the random forest, we determine the final gray scale by keeping the balance between intensity and luminance channels. In our experiments, we show that the proposed method outperforms the state-of-the-arts in view of color constrast preserving ratio and mean squared error versus luminance.