Automatic identification of fictional characters is one of the primary analysis techniques for video content. A common approach to detect characters in live-action movies involves detecting human faces; however, this approach cannot be used in non-realistic domains, such as animated movies. Detection of characters in animated movies presents two major challenges: the same subject of character can be expressed in various unique styles, and there are no stylistic or other restrictions on the nature and design of character objects. To address these challenges, we introduce the “animation adaptive region-based convolutional neural network” model to detect characters in animated movies and determine whether the detected characters are human or non-human types. Our model extends the Faster R-CNN model, which is a two-stage object detector, in the following manner: 1) we add a hierarchical animation adaptation module to learn the variety of unique styles from animated movies using a single model; 2) we incorporate a double-detector architecture to focus on the regions that are visually important in determining the character class. We build a new dataset for the animated character detection task. Experiments on this dataset show that our model outperforms other existing representative object detector models in terms of character detection. Furthermore, our model achieves significant performance improvements compared with previous state-of-the-art methods used for the character dictionary generation task. Our model is robust for a variety of animation styles and can find common visual representations of all types of characters, providing an effective way to detect animated characters.

The piracy of copyrighted digital content over the Internet infringes copyrights and damages the digital content industry. Accordingly, identifying and monitoring technology on the online content service like fingerprinting is getting valuable through the explosion of digital content sharing. This paper proposes a robust video fingerprinting feature to identify a modified video clip from a large scale database. Hierarchical symmetric difference feature is proposed in order to offer efficient video fingerprinting. The feature is robust and pairwise independent against various video modifications such as compression, resizing, or cropping. Moreover, videos undergoing a transformation such as flipping or mirroring can be identified by simply disordering the bit pattern of fingerprints. The performance of the proposed feature is extensively experimented on 6,482 hours of database and the experimental results show that the proposed fingerprinting is efficient and robust against various modifications.