Deep Learning for Real-time Vehicle Detection through Deep Recurrent Neural Networks – Although the human visual system has proven to perform very well on a broad range of tasks, it remains a challenging task and difficult domain for machine vision (MVD). In this paper we propose a new deep-learning approach to MVD for video. The proposed approach consists in utilizing a recurrent neural network (RNN), which automatically learns a sequence representation based on the visual information (saccades), and a deep-learning neural network (DNN). The discriminative and discriminative networks are trained to predict video sequences. A novel training scheme, which is jointly optimized with the deep networks, is presented. Experiments on the recently proposed dataset BUG3 show that our approach outperforms previous methods by a considerable margin when compared to the state-of-the-art MVD approaches.
This paper presents a tool called BISNAP. It is a software package that supports the detection of objects with semantic and spatial information. In this paper, a set of data of a person and its objects are extracted with semantic information, and then processed by a machine learning algorithm using a semantic detector. The semantic detector is designed to evaluate the semantic information of a person and the object at each part of the problem. The system was implemented on Android platform. BISNAP is a software package that supports the detection of objects with semantic and spatial information. In this paper, a set of data from a person and its objects are extracted with semantic information, and then processed by a machine learning algorithm using a semantic detector. The system was implemented on Android platform. BISNAP is a software package that supports the detection of objects with semantic and spatial information. Since the semantic detectors in BISNAP is a combination of semantic and spatial information, the algorithm is able to compare the semantic detector performance using different semantic and spatial information. This paper presents an implementation of this algorithm.
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Deep Learning for Real-time Vehicle Detection through Deep Recurrent Neural Networks
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Proceedings of the 2016 ICML Workshop on Human Interpretability in Artificial IntelligenceThis paper presents a tool called BISNAP. It is a software package that supports the detection of objects with semantic and spatial information. In this paper, a set of data of a person and its objects are extracted with semantic information, and then processed by a machine learning algorithm using a semantic detector. The semantic detector is designed to evaluate the semantic information of a person and the object at each part of the problem. The system was implemented on Android platform. BISNAP is a software package that supports the detection of objects with semantic and spatial information. In this paper, a set of data from a person and its objects are extracted with semantic information, and then processed by a machine learning algorithm using a semantic detector. The system was implemented on Android platform. BISNAP is a software package that supports the detection of objects with semantic and spatial information. Since the semantic detectors in BISNAP is a combination of semantic and spatial information, the algorithm is able to compare the semantic detector performance using different semantic and spatial information. This paper presents an implementation of this algorithm.