There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.
Today, kidney medical imaging has become the backbone for health professionals in diagnosing kidney disease and determining its severity. Physicians commonly use Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) scan models to obtain kidney disease information. The significance and impact of kidney tumor analysis drew researchers to semantic segmentation of kidney tumors. Traditional image processing methodologies, in general, require more computational power and manual assistance to analyze kidney medical images for tumor segmentation. Deep Learning advances are enabling less computational and automated models for kidney medical image analysis and tumor lineation. Blobs (regions of interest) detection from medical images is gaining popularity in kidney disease diagnosis and is used widely in detecting tumors, glomeruli, and cell nuclei, among other things. Kidney Tumor segmentation is challenging compared to other segmentation models due to morphological diversity, object overlapping, intensity variance, and integrated noise. In this paper, It have proposed a kidney tumor semantic segmentation model based on CU-Net and Mask R-CNN to extract kidney tumor information from abdominal MR images. Initially, It trained the Custom U-Net architecture on abdominal MR images with kidney masks for kidney image segmentation. The Mask R-CNN model is then used to lineate tumors from kidney images. Experiments on abdominal MR images using Python image processing libraries revealed that the proposed deep learning architecture segmented the kidney images and lined up the tumors with high accuracy.
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