id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-028792-6a4jfz94	Basly, Hend	CNN-SVM Learning Approach Based Human Activity Recognition	2020-06-05		.txt	text/plain	3570	178	49	Traditionally, to deal with such problem of recognition, researcher are obliged to anticipate their algorithms of Human activity recognition by prior data training preprocessing in order to extract a set of features using different types of descriptors such as HOG3D [1] , extended SURF [2] and Space Time Interest Points (STIPs) [3] before inputting them to the specific classification algorithm such as HMM, SVM, Random Forest [4] [5] [6] . In this study, we proposed an advanced human activity recognition method from video sequence using CNN, where the large scale dataset ImageNet pretrains the network. Finally, all the resulting features have been merged to be fed as input to a simulated annealing multiple instance learning support vector machine (SMILE-SVM) classifier for human activity recognition. We proposed to use a pre-trained CNN approach based ResNet model in order to extract spatial and temporal features from consecutive video frames.	./cache/cord-028792-6a4jfz94.txt	./txt/cord-028792-6a4jfz94.txt