LAUSR

As the demand for large-scale video analysis increases, video retrieval research is also becoming more active. In 2014, ISO/IEC MPEG began standardizing compact descriptors for video analysis, known as CDVA, and it is now adopted as a standard. However, the standardized CDVA is not easily compared to other methods because the MPEG-CDVA dataset used for performance verification is not disclosed, despite the fact that follow-up studies are underway with multiple versions of the CDVA experimental model. In addition, analyses of modules constituting the CDVA framework are insufficient in previous studies. Therefore, we conduct self-evaluations of CDVA to analyze the impact of each module on the retrieval task. Furthermore, to overcome the obstacles identified through these self-evaluations, we suggest temporal nested invariance pooling, abbreviated as TNIP, which implies temporal robustness realized by improving nested invariance pooling, abbreviated as NIP, one of the features in CDVA. Finally, benchmarks of the existing CDVA and the proposed approach are provided on several public datasets. Through this, we show that the CDVA framework is capable of boosting the retrieval performance if utilizing the proposed approach.