An Action Probability Model for Event Detection from Data Streams


An Action Probability Model for Event Detection from Data Streams – Object-Poster system (PDS) provides a mechanism for generating a set of candidate objects based on a set of information encoded from object images. In this paper, we propose an efficient PDS method based on a fully-validated classifier. The proposed method is capable of generating a set of objects from images and its training weights are computed using the classification procedure of PDS. We give a comprehensive analysis of the results of our method and show the efficiency and benefits of our method. We will make several further experiments on two challenging datasets: two image classification tasks and two retrieval tasks using a dataset with 30000 images. The two tasks achieve a good performance in both performance tests. Our method does not need any additional parameter or additional training set. We are currently investigating the performance of our method as a data-mining technique, which can improve our ability to generate candidate objects for objects images by learning new features.

We present an application of deep learning techniques for automatic gesture recognition from facial expressions. Based on convolutional neural networks (CNNs), we propose a novel deep learning model for object detection which addresses the problem of using object detection to detect occluded objects. We leverage a Convolutional Neural Network (CNN) to learn the object features in a CNN, and then train the object detector to find the occluded objects. To test our model on real-world applications, we perform this challenge against three different datasets based on the human test set. The test sets are of different kinds and in each case, object recognition was achieved at different difficulty levels compared to the other hand. Furthermore, we demonstrate that our method is capable of detecting objects that are more similar to the human than to similar objects.

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An Action Probability Model for Event Detection from Data Streams

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    DeepKSPD: Learning to detect unusual motion patterns in videosWe present an application of deep learning techniques for automatic gesture recognition from facial expressions. Based on convolutional neural networks (CNNs), we propose a novel deep learning model for object detection which addresses the problem of using object detection to detect occluded objects. We leverage a Convolutional Neural Network (CNN) to learn the object features in a CNN, and then train the object detector to find the occluded objects. To test our model on real-world applications, we perform this challenge against three different datasets based on the human test set. The test sets are of different kinds and in each case, object recognition was achieved at different difficulty levels compared to the other hand. Furthermore, we demonstrate that our method is capable of detecting objects that are more similar to the human than to similar objects.


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