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We present a novel deep learning framework for crowd counting by learning a perspective-embedded deconvolution network. Perspective is an inherent property of most surveillance scenes. Unlike the traditional approaches that exploit the perspective as a separate normalization, we propose to fuse the perspective into a deconvolution network, aiming to obtain a robust, accurate and consistent crowd density...
Robust visual tracking for outdoor vehicle is still a challenging problem due to large appearance variations caused by illumination variation, occlusion and scale variation, etc. In this paper, a deep-learning-based approach for robust outdoor vehicle tracking is proposed. Firstly, a stacked denoising auto-encoder is pre-trained to learn the feature representation way of images. Then, a k-sparse constraint...
This paper introduces a dataset of historical images created by the State Library of New South Wales and the University of Technology Sydney (UTS). The dataset has a total of 29713 images with 119 unique labels. Each image contains multiple labels. We use a CNN-based framework to explore the feasibility of our dataset in image multi-labeling and retrieval research, and extract semantic level image...
One important problem in machine learning is how to extract knowledge from prior experience, then transfer and apply this knowledge in new learning tasks. To address this problem, transfer learning leverages information from (supervised) learning on related tasks to facilitate the current learning task. Self-taught learning uses information extracted from (unsupervised) learning on related data. In...
A robust framework to classify vehicles in nighttime traffic using vehicle eigenspaces and support vector machine is presented. In this paper, a systematic approach has been proposed and implemented to classify vehicles from roadside camera video sequences. Collections of vehicle images are analyzed to obtain their representative eigenspaces. The model Support Vector Machine (SVM) built from those...
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