Across diverse surveillance, rescue, and scientific research applications, there is a specific need to perform on-the-ground target object detection and localization from highly efficient aerial platforms. The aerial target geolocation task has seen rapid development in recent years, contributed by the explosive growth of unmanned aircraft vehicles (UAVs) and artificial intelligence (AI) computing. The existing solutions of using the monocular camera as the vision-based geolocation sensor exhibited a high chance of geolocation error and inconvenience requirement of pre-operation calibration of the camera's intrinsic parameter. Studies on the potential of using stereo vision technology for geolocating targets from aerial platforms are still scarce, especially when integrated with automated detection and edge processing. This study aimed to develop a stereo vision-based aerial target geolocation system with excellent effectiveness and operational flexibility. This research explores three modules: (1) stereo vision-based geolocation modeling, (2) deep learning-based object detection modeling, and (3) evaluation of edge computer performance. The novel stereo vision-based aerial target geolocation algorithm is formulated by using a constructive model development technique, whereby the stereo vision point cloud information of the detected target is used as the distance and angle parameters in the radar target tracking model and projected coordinate system. The relevant Darknet-based object detection models are evaluated, each trained with the VisDrone2018 aerial training dataset. The detection and geolocation module has been executed in the Nvidia Jetson TX2 edge computing platform to determine the system's feasibility. The evaluation and validation of the proposed geolocation, detection, and processing modules are performed using the ground-based platform field test experiments and MATLAB. The field experiment results validated the proposed stereo vision-based aerial target geolocation, demonstrating the geolocation accuracy of 0.53-meter mean error at the 5-meter testing height. Besides, the selected detection module, the YOLOv4 model, scored a detection accuracy of 30.69% mean Average Precision (mAP) value when tested on the official test tool, and its detection speed satisfied the safety requirements of 2 frames per second (FPS) for aerial applications. Further, the edge computer platform exhibited minimal power consumption of 5 Watts and maintained the manufacturer's system operating standards. These findings demonstrate the feasibility of the proposed stereo vision based aerial target geolocation system to be used as a system payload for aerial platforms and serve as a reference framework for Search and Rescue (SAR) agencies in detecting distressed humans.