We present a fast and efficient approach for joint person detection and pose estimation optimized for automated driving (AD) in urban scenarios. We use a multitask weight sharing architecture to jointly train detection and pose estimation. This modular architecture allows us to accommodate different downstream tasks in the future. By systematic large-scale experiments on the Tsinghua-Daimler Urban Pose Dataset (TDUP), we obtain multiple models with varying accuracy-speed trade-offs. We then quantize and optimize our network for deployment and present a detailed analysis of the efficacy of the algorithm. We introduce a two-stage evaluation strategy, which is more suitable for AD and achieve a significant performance improvement in comparison to state-of-the-art approaches. Our optimized model runs at 52~fps on full HD images and still reaches a competitive performance of 32.25~LAMR. We are confident that our work serves as an enabler to tackle higher-level tasks like VRU intention estimation and gesture recognition, which rely on stable pose estimates and will play a crucial role in future AD systems.

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Prostate cancer is the leading cause of death for men, since the cause of the disease is mysterious and its early detection is also monotonous. Ultrasound (US) is the most popular tool to detect the human organ glands and also used to diagnose the prostate cancer. Speckle noise is an inherent nature of ultrasound images, which degrades the image quality. So far, No specific filter is available to suppress the speckle noise in prostate image. In this paper, a novel despeckling method PDE with Wavelet is presented for prostate US images. The enhancement method is evaluated by using standard measures like Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Edge Preservation Index (EPI). Further, the despeckling approaches' is also evaluated time and space complexity. From the results, it is observed that the filtering method PDE with Wavelet is superior to PDE in terms of denoising and also preserving the information content.

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