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This paper introduces an approach to employ deep features for person re-identification. In contrast to existing works, we focus on using pre-trained deep models and their concept-based output to enhance attribute presentations of person images. There are two main contributions. First, we investigate recent state-of-the-art deep learning models for the task and provide a comprehensive evaluation. Second,...
The rapid development of intelligent transport systems (ITS) brings us a safer and more convenient life. In this field, automatic license plate recognition (ALPR) plays an important role in many applications which have been deployed in reality such as stolen car detection, parking system management, and automatic transport charging system. The traditional ALPR methods usually need a high resolution...
Multi-frame super-resolution brings out much potential to reconstruct real high-resolution video sequences. This potential is achieved based on its capacity to combine missing information from different input low-resolution frames. Although there have been many studies in recent decades, super-resolution problems for real-world video processing still have many challenges. This is dues to two problems...
Person Re-Identification problem aims at matching people across a network of non-overlapping cameras. When multiple probe people appear concurrently, human could compare them together to give a more accurate matching. However, existing approaches treat each probe person independently, skipping the concurrent information. In this paper, we propose a re-ranking method which utilize that kind of information...
Sensor networks have been a very active area of research in recent years. However, most of the sensors used in the development of these networks have been local and non-imaging sensors such as acoustics, seismic, vibration, temperature, humidity, etc. The development of emerging video sensor networks poses its own set of unique challenges, including high bandwidth and low latency requirements for...
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