This article presents two methods, REVAMPT and CARPe Posterum. REVAMPT, or Real-time Edge Video Analytics for Multi-person Privacy-aware Tracking, is an integrated system for privacy-built-in pedestrian re-identification. REVAMPT presents novel algorithmic and system constructs to push deep learning capabilities for pedestrian re-identification at the edge (i.e. the video camera). On the algorithm side, REVAMPT proposes a unified computer vision pipeline for detection and re-identification on a low-power computing device without the need for storing the streaming data. At the same time, it avoids facial recognition, re-identifying pedestrians based on their encoded key features at runtime. For the results and evaluation, this article also proposes a new metric, Accuracy·Efficiency (Æ), for holistic evaluation of deployable systems based on accuracy, performance, and power efficiency. REVAMPT outperforms current state-of-the-art by as much as ten-fold Æ improvement. A symbiotic task for pedestrian re-identification is path prediction, and therefore we also propose CARPe Posterum, a Convolutional Approach for Real-time Pedestrian Path Prediction. Having insight into the movement of pedestrians is not only important for pedestrian re-identification, but also for ensuring safe operation in a variety of applications including autonomous vehicles and social robotics. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, CARPe utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS by at least 8x in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.