In the framework of 3D computer vision dedicated to assistive technologies, the research studies reported in this thesis have the objective to design new computer-vision-based approaches dedicated to embedded and real-time applications with limited resources. This thesis proposes novel strategies for rapid object detection and recognition under practical constraints. These limitations are for example the number of sensors and their resolution, algorithm complexity and mobile battery-life. We narrowed the research scope to specific objects and obstacles detection from off-the-shelf stereo and plenoptic cameras. The research work thus investigates two areas of computer vision from multi-view imaging, namely exploiting (i) sparse 3D keypoint clouds from stereo vision and (ii) light field imaging for low-complexity and efficient algorithms.