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.

We consider the problem of learning sequential representations of data by leveraging sequential information for learning. In this paper, we establish a link between sequential and sequential knowledge via a connectionist framework using a novel set of constraints: given a dataset containing a subset of labels, an optimal sequence is selected by minimizing the minimum probability of all labels (i.e., the probability that the label is in the correct set). By combining the constraints with sequential knowledge, we infer sequential representations as a set of constraints. We show how this strategy, called sequential knowledge representation learning, can be extended to a set of more formal constraints and we show how to efficiently learn the sequential representations via sequential learning. We show how our approach can be used to guide downstream learning algorithms, such as classifiers, that use multiple constraints as a weight when learning. We provide theoretical and computational bounds on sequential knowledge representation learning and show how to use it to optimize a deep learning framework. Through experiments, we demonstrate that in some scenarios sequential knowledge representation learning helps reduce the computation cost of a sequential classification algorithm.

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Deep Learning for Multi-Person Tracking: An Evaluation

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  • Deep-MNIST: Recurrent Neural Network based Support Vector Learning

    Generative Closure Networks for Deep Neural NetworksWe consider the problem of learning sequential representations of data by leveraging sequential information for learning. In this paper, we establish a link between sequential and sequential knowledge via a connectionist framework using a novel set of constraints: given a dataset containing a subset of labels, an optimal sequence is selected by minimizing the minimum probability of all labels (i.e., the probability that the label is in the correct set). By combining the constraints with sequential knowledge, we infer sequential representations as a set of constraints. We show how this strategy, called sequential knowledge representation learning, can be extended to a set of more formal constraints and we show how to efficiently learn the sequential representations via sequential learning. We show how our approach can be used to guide downstream learning algorithms, such as classifiers, that use multiple constraints as a weight when learning. We provide theoretical and computational bounds on sequential knowledge representation learning and show how to use it to optimize a deep learning framework. Through experiments, we demonstrate that in some scenarios sequential knowledge representation learning helps reduce the computation cost of a sequential classification algorithm.


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