# Fast Object Detection in Light Field Imaging by Integrating Deep Learning with Defocusing  
[PERSON]  
1 State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering,  
Shanghai Jiao Tong University, Shanghai 200240, China; [EMAIL_ADDRESS] (M.R.);  
[EMAIL_ADDRESS] (R.L.);  
[EMAIL_ADDRESS] (J.R.);  
[EMAIL_ADDRESS] (G.X.)  
1 State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering,  
Shanghai Jiao Tong University, Shanghai 200240, China; [EMAIL_ADDRESS] (M.R.  
accurately than conventional stereo vision [10; 11; 12; 13]. In light field imaging, depth estimation methods may generally be divided into two categories, including depth from stereo and depth from defocus. When comparing with stereo vision, the light field has unique ability to expand of the disparity space to a continuous space [11]. Hence, digital refocusing algorithm can be implemented and defocus response becomes additional cue to estimate the depth of the scene. For defocus cues, it avoids the occlusion problem [14] and matching ambiguity problems [15]. Traditionally, the defocus cue has been achieved through using multiple image exposures [16], which is however too slow to be used in recognition. Fortunately, the light field inherent refocusing ability can obtain a series of images that focus on different depth with highly parallel computation. This makes the fast estimation of the depth from defocus possible. Several research has also been conducted to combine both the disparity and defocus response so that the accuracy of the estimation can be further improved [17; 18].  
In view of the published literature, it is noticed that the most of the existing depth estimation methods for light field imaging are computationally too expensive to be used in real time application. Although much accurate estimation of the depth of the scene can be achieved comparing with conventional stereo vision, it may take several minutes for producing a single scene on a current general purpose computer, which is unacceptable for many application, such as augmented reality, object detection and tracking, and so on. In many applications, instead of the whole scene, high accurate depth estimation may only be required at specific regions, such as the regions where the objects are located. In such scenario, machine learning would become a good choice to recognize the objects, and sparse depth estimation can then be performed at the specified regions. In the recent decade, deep neural network has been being boom up in recognition and localization of objects from an image [19; 20; 21; 22; 23]. Therefore, this paper presents a hybrid method to accelerate the object detection in light field imaging to real time level. The main contribution of the presented work includes a framework for fast object detection and depth estimation in light field imaging by integrating the deep learning with the depth estimation algorithm. An algorithm is also presented to further optimize the depth of the detected object that is based on multi-baseline stereo matching while efficiency is maintained as well by compressing the searching space of the disparity using the defocus response. Details of the methods are presented and experimental studies are conducted to verify the effectiveness of the proposed method.