Human Tracking of a Crawler Robot in Climbing Stairs

There are many reports of secondary damage to crews during firefighting operations. One way to support and enhance their activities is to get robots to track them and carry supplies. In this paper, we propose a localization method for stairs that includes scene detection. The proposed method allows a robot to track a person across stairs. First, the scene detection autonomously detects that the person is climbing the stairs. Then, the linear model representing the first step of the staircase is combined with the person’s trajectory for localization. The method uses omnidirectional imaging and point clouds, and the localization and scene detection are available from any posture around the stairs. Finally, using the localization result, the robot automatically navigates to a posture where it can climb the stairs. Verification confirmed the accuracy and real-time capability of the method and demonstrated that the actual crawler robot autonomously chooses a posture that is ready for climbing.

Context-Based Structure Mining Methodology for Static Object Re-Identification in Broadcast Content

Technological advancement, in addition to the pandemic, has given rise to an explosive increase in the consumption and creation of multimedia content worldwide. This has motivated people to enrich and publish their content in a way that enhances the experience of the user. In this paper, we propose a context-based structure mining pipeline that not only attempts to enrich the content, but also simultaneously splits it into shots and logical story units (LSU). Subsequently, this paper extends the structure mining pipeline to re-ID objects in broadcast videos such as SOAPs. We hypothesise the object re-ID problem of SOAP-type content to be equivalent to the identification of reoccurring contexts, since these contexts normally have a unique spatio-temporal similarity within the content structure. By implementing pre-trained models for object and place detection, the pipeline was evaluated using metrics for shot and scene detection on benchmark datasets, such as RAI. The object re-ID methodology was also evaluated on 20 randomly selected episodes from broadcast SOAP shows New Girl and Friends. We demonstrate, quantitatively, that the pipeline outperforms existing state-of-the-art methods for shot boundary detection, scene detection, and re-identification tasks.

Design and Implementation of Robust Digital Watermarking using Hybrid technique for Copyright Protection Digital data

In this research paper, we concerned with the creation of a comprehensive digital watermarking framework based on DWT. In order to improve imperceptibility and robustness, the watermark is inserted only in chosen frames. The picked frames are the frames in which a change of scene happens. The key objective, therefore, is to detect the correct transformation of the scene. The Scene Shift Detector identifies correct frames that have been modified using the successive histogram discrepancy process. Two schemes proposed using the same method of scene detection. Both suggested schemes achieve a good watermark rating with good (PSNR) valuesThere, Because the watermark integration is done exclusively on the scene with low and high frequency DWT subbands, the image processing assaults, geometric aggressions, JPEG compression, high normalised image attacks are immune,and low-bit error rates (BER). Comparative analysis of two algorithms is also carried out.