Deep Learning for Multi-Person Tracking: An Evaluation


Deep Learning for Multi-Person Tracking: An Evaluation – In this paper, we focus on the task of object detection under various types of illumination. To this end, in this paper, we present new methods for object detection under different illumination conditions. These methods include the use of deep convolutional layers and methods that learn the features from deep object detectors without requiring access to object data. Our main contribution is to show that our proposed methods can achieve the best performance under particular conditions given the data distribution of the camera.

Deep learning (DL) is one of the most influential approaches to various computer vision tasks. The key ingredient of DL models that can be learned is labeled objects as being similar in some way to the object’s appearance. In this paper, we present a system for object recognition under different lighting conditions, where the camera is at a high level, and the object is similar to the object that was observed. Experimental results on the PASCAL VOC dataset show that the model learned under an illumination cue has superior performance than the current state of the art models in terms of accuracy and time complexity, as shown by experimental results on the other datasets.

A key problem in many computer vision applications is the detection or segmentation of unknown objects from image. In order to tackle this challenge, we propose a novel and efficient clustering algorithm for object identification and extraction. The key idea is to first learn a graph-theoretic model of different clusters, and then train a Convolutional Neural Net (CNN) to recognize unknown objects (cannot distinguish any object). In this work, we propose a novel supervised learning approach that trains CNN-based models, and then optimizes their performance by minimizing the dimension of the network and the number of objects. We demonstrate that this approach outperforms both traditional clustering methods and our best-known segmentation method, i.e. kLSTM-SVM, in several real-world datasets, and is faster than the best known method.

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Deep Learning for Multi-Person Tracking: An Evaluation

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  • Unsupervised Deep Learning With Shared Memory

    Cortical-based hierarchical clustering algorithm for image classificationA key problem in many computer vision applications is the detection or segmentation of unknown objects from image. In order to tackle this challenge, we propose a novel and efficient clustering algorithm for object identification and extraction. The key idea is to first learn a graph-theoretic model of different clusters, and then train a Convolutional Neural Net (CNN) to recognize unknown objects (cannot distinguish any object). In this work, we propose a novel supervised learning approach that trains CNN-based models, and then optimizes their performance by minimizing the dimension of the network and the number of objects. We demonstrate that this approach outperforms both traditional clustering methods and our best-known segmentation method, i.e. kLSTM-SVM, in several real-world datasets, and is faster than the best known method.


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