A Novel Multiclass Object Detection Dataset Enriched With Frequency Data

Abstract

Video analysis has attracted the attention of many researchers because of the growing need for multimedia information retrieval for computer vision applications. Content based retrieval and object-based video retrieval are challenging because of the poor feature representation of the objects and the low number of multiclass video datasets available. Even though there have been significant advances in generic object detection & information retrieval tasks, performance of object based indexing & retrieval falls due to the low number of representative video dataset and lack of information on the frequency of objects. In order to facilitate the progress of multimedia information retrieval in the field of computer vision related application research, we present the multiclass object detection datasets. This dataset consists of 732 videos from YouTube with 5 object classes and each video is about 2 minutes long and total size of the dataset being approximately 8.5 GB. In this article, we will provide the baseline evaluation results of two of the latest object detection algorithms used to evaluate the newly created multidimensional object detection dataset. The paper presents the performance metrics of object detection methods at various time frames. Accuracy remains consistently high, ranging from 93.98% at the 5th frame to 84.17% at the 30th frame, suggesting a generally strong ability to make correct predictions. The dataset is publicly available at https://drive.google.com/file/d/1BHVsB38vbu9LUY03XlFWY2o_gxEiIKY7/view?usp=sharing.

Keywords

• Retrieval
• indexing
• object detection