A Performance Evaluation on Action Recognition with Local Features

Local features have played an important role in visual recognition. Methods based on local features, e.g., the bag-of-words (BoW) model and sparse coding, have shown their effectiveness in image and object recognition in the past decades. Recently, many new techniques, including the improvements of BoW and sparse coding as well as the non-parametric naive bayes nearest neighbor (NBNN) classifier, have been proposed and advanced the state-of-the-art in the image domain. However, in the video domain, the BoW model still dominates the action recognition field. It is unclear how effective the state-of-the-art techniques widely used in the image domain would perform on action recognition. To fill this gap, we aim to implement and provide a systematic study of these techniques on action recognition, and compare their performance under a unified evaluation framework. Other techniques such as match kernels and random forest, which have also demonstrated their potential in handling local features, are also included for a comprehensive evaluation. Extensive experiments have been conducted on three benchmarks including the KTH, the UCF-YouTube and the HMDB51 datasets, and results and findings are analyzed and discussed.