A User-selectable Obscuration Framework to Censor Digital Videos for Children and Adolescents Jiayan GUO, David LEONG, Jonathan SIANG, and Vikram BAHL School of Infocomm, Republic Polytechnic, Singapore ABSTRACT storyline of the film. Such harmful content is still accessible to the viewers. The children thus are exposed to There is an increasing concern from parents, educators a vast amount of inappropriate media content. and policy-makers about the negative influence that digital media exerts on children and adolescents. Such According to the Singapore Censorship Review concerns have fueled a growing need to effectively filter Committee 2010 report [2], parents are encouraged to take potentially harmful content. However, existing responsibility to protect their children against the negative technologies have limited ability in allowing users to aspects of media proliferation. In order to guide their adjust the filtering levels, or generating seamless cutting children on digital media consumption, parents have to be results. To tackle this limitation, we propose a framework empowered with effective tools to filter sex, violence and which empowers parents and teachers to censor movies profanity out of the digital media. However, existing tools and TV shows according to their level of acumen and have limited capabilities to effectively block potentially discretion. Such framework helps parents and teachers harmful content. Presently, ClearPlay and MovieMask are protect children and adolescents against obscene content. the two forefront computer programs that cleanse movies In particular, our framework enables parents and teachers containing offensive scenes. Their technologies are built to sanitize movies and TV shows by skipping over onto stand-alone DVD players and other video devices. specific objectionable scenes. Moreover, the framework While both can mask objectionable content, their technical can blur out the unsavory objects in the scenes, so that the operations and capabilities are considerably different. integrity of the storyline can be preserved. Technically, it Specifically, for ClearPlay, users have to download two utilizes the non-rigid object tracking and video masking components, including the software and a filter associated techniques to blur out the unwanted object. Instead of with a particular DVD movie. The filter can guide the physically altering the original videos or making DVD player to mute dialogues or skip scenes during replicated copies, our framework keeps the original video playback of the corresponding movie. Such filter is pre- unscathed by applying the censorship to the video during defined, and users cannot customize it according to their playback. We conducted evaluations on the challenging needs. In contrast, MovieMask allows users to personalize real-world video sequences. The experimental results the blocking of harmful content. Technically, it first lets demonstrated effectiveness of the proposed framework. users select the edited scenes, and some graphics/animations. It then censors the movie by Keywords: Video Censorship, Non-Rigid Object overlaying the graphics/animations onto the selected Tracking, Mean Shift, Scale and Orientation Adaptive, scenes. However, this technology affects the integrity of Video Masking. the original movie. The resultant censored movie would be jagged, and the scene-to-scene cuts are noticeable. 1. INTRODUCTION Motivated by above observations, we propose a novel framework to facilitate parents and teachers to censor the With the rapid development in information technology, movies and TV shows. The framework enables parents children and adolescents have unprecedented access to and teachers to select the obscuration according to their digital media. Given the double-edged sword digital level of acumen and discretion. With our framework, media has become, parents, teachers and policymakers parents and teachers can sanitize the movies and TV have concerns about the negative impact that digital media shows by skipping over specific scenes that contain nudity, exerts on children and adolescents. Many parents believe sexual situations and excessive violence. Moreover, our that digital media is a major contributor to young people’s framework can also be used to blur out the unsavory violent or sexual behaviors [1]. This leads to a growing objects in the scenes, which is effective to preserve the need for digital media content regulation and censorship. continuity of the storyline. Take the painting scene in However, current censorship is mild and insufficient. It is "Titanic" with Kate Winslet posing nude as an example, only applied to a small number of films or TV shows that our framework allows users to pause the video in the have explicit sensitive or offensive content. Over the last current frame and select her body as the target area to be three years, out of 2,351 films classified, only nine films blurred out. It then automatically tracks the target area (0.4%) were censored. Many violence and sexual scenes throughout the video based on the non-rigid object are still allowed if they are relevant to the theme and tracking technique. After the target area is located, our This work was supported by grant MOE2011-TIF-1-G-019 from the Ministry of Education, Singapore. framework applies the video masking technique to blur ask them to seek the objectionable scenes. The users can out the target area in every frame it appears. Finally, the pause the video and select the unsavory object in the censored video is presented to user during playback. current frame as a target area (also known as Region of Different from existing sanitizing tools, such as Interest). We then employ a non-rigid object tracking CleanFlicks1 and Family Flix, our framework does not algorithm to find the target area throughout the video. physically alter the original videos or make alteration Afterwards, a video masking technique is utilized to blur copies. The censorship is only applied to the video during out that target area throughout the frames covered. Finally, the video playback. That is, the original video remains we play back the censored video to the users. untouched and thus does not violate any copyright issues. In this paper, we present the following three contributions: Firstly, our framework empowers parents and teachers to filter harmful content out of movies or TV shows based on their own standards and preferences. Secondly, the non- rigid object tracking and video masking techniques in our framework facilitate users to blur out the unsavory objects. In particular, the non-rigid object tracking technique outperforms the current state-of-the-art approaches. It has low computational complexity and is easy to be implemented. In addition, it is capable of handling large variety of objects with different color/texture patterns, being robust to partial occlusions, significant clutter, target scale variations, rotation in depth, and changes in camera position. The video masking technique is employed to blur out the objectionable target area. It is not only effective in blocking unwanted gore and salacious content for young viewers, but it preserves the veracity of the entire film, leaving its artistic vision intact. Lastly, since our framework does not physically alter the original videos or make modification copies, it does not infringe on any copyright law. The rest of the paper is organized as follows. Section II provides a brief overview of our framework. The details of the non-rigid object tracking algorithm used in our Fig. 1. Procedure of blurring unsavory objects in the scenes. framework is elaborated in Section III. In Section IV, the video masking is described in detailed. Section V provides In the next two sections, we will discuss the non-rigid a snapshot of our framework and reveals the experimental object tracking algorithm and video masking technique. results. In addition, our non-rigid object tracking algorithm is compared against two popular tracking algorithms. We evaluate the experimental results we have 3. NON-RIGID OBJECT TRACKING ALGORITHM obtained. Finally, we conclude this paper in Section VI. Object tracking is a fundamental but challenging task in the field of computer vision. There are sources of 2. OUR FRAMEWORK uncertainty in tracking the real-world videos that render it a highly non-trivial task, such as complex scene Our framework aims to filter the potentially harmful clustering, partial/full occlusions, non-rigid object content out of videos according to the preferences of deformation, and illumination change. A number of parents and teachers. The filtering can either be skipping algorithms have been proposed to overcome these over the objectionable scenes, or blurring out the unsavory difficulties. Among various tracking algorithms, the mean objects in the scenes. shift algorithm is well-known due to its simplicity and efficacy. The mean shift algorithm was firstly developed To skip over objectionable scenes, we first segment the by Fukunaga and Hostetler [3] for data analysis. It was videos into frames, and ask the users to seek some frames later introduced into the field of image processing by as the potentially objectionable scenes. The videos are Cheng [4]. Comaniciu et al. [5] had successfully applied then censored by removing the objectionable scenes, and the mean shift algorithm to object tracking. However, in shown to users during playback. Such skipping may affect the classical mean shift tracking algorithm [5], the the continuity of the video storyline. To enhance the estimation of target scale and orientation changes was not censoring, we resort to the filtering strategy of blurring solved. Bradski [6] further modified the mean shift unsavory objects in the scenes. We illustrate the blurring tracking algorithm and developed the Continuously strategy in Fig.1. The strategy consists of four steps. We Adaptive Mean Shift (CAMSHIFT) algorithm. The first invite the users to watch the movie or TV shows, and moment of the weight image determined by target model 1 found to be illegal by a 2006 District of Colorado court ruling. is used to estimate the object scale and orientation. m ˆ()y [(),]pˆ y qpˆˆˆ ()y q .