Joint Object Detection and Depth Estimation in Multiplexed Image

This paper presents an object detection method that can simultaneously estimate the positions and depth of the objects from multiplexed images. Multiplexed image is produced by a new type of imaging device that collects the light from different fields of view using a single image sensor, which is originally designed for stereo, 3D reconstruction and broad view generation using computational imaging. Intuitively, multiplexed image is a blended result of the images of multiple views and both of the appearance and disparities of objects are encoded in a single image implicitly, which provides the possibility for reliable object detection and depth/disparity estimation. Motivated by the recent success of CNN based detector, a multi-anchor detector method is proposed, which detects all the views of the same object as a clique and uses the disparity of different views to estimate the depth of the object. The proposed method is interesting in the following aspects: firstly, both locations and depth of the objects can be simultaneously estimated from a single multiplexed image; secondly, there is almost no computation load increase comparing with the popular object detectors; thirdly, even in the blended multiplexed images, the detection and depth estimation results are very competitive. There is no public multiplexed image dataset yet, therefore the evaluation is based on the simulated multiplexed image using the stereo images from KITTI, and very encouraging results have been obtained.