Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition

Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition

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Human action recognition has gathered significant attention BOSCH Serie 4 BFL553MB0B Built-in Solo Microwave - Black in recent years due to its high demand in various application domains.In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos.The proposed scheme develops a discriminative codebook and a hybrid feature vector by encoding the features extracted from CNNs (convolutional neural networks).

We explore different CNN architectures for extracting spatio-temporal features.We employ an agglomerative clustering approach for codebook generation, which intends to combine the advantages of global and class-specific codebooks.We propose a Residual Vector of Locally Aggregated Descriptors (R-VLAD) and fuse it with locality-based coding to form a hybrid feature vector.

It provides a compact representation along with high order statistics.We evaluated our work on two publicly available standard benchmark datasets HMDB-51 and UCF-101.The proposed method achieves 72.

6% and 96.2% on HMDB51 and UCF101, respectively.We conclude that the proposed scheme is Tea Pot able to boost recognition accuracy for human action recognition.