FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System.

Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from the perspective of camera based deep learning algorithms. We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture. We demonstrate a real-time multi-task deep learning network called FisheyeMultiNet, which detects all the necessary objects for parking on a low-power embedded system. FisheyeMultiNet runs at 15 fps for 4 cameras and it has three tasks namely object detection, semantic segmentation and soiling detection. To encourage further research, we release a partial dataset of 5,000 images containing semantic segmentation and bounding box detection ground truth via WoodScape project [Yogamani et al., 2019]

Structured output SVM prediction of apparent age, gender and smile from deep features

© 2016 IEEE. We propose structured output SVM for predicting the apparent age as well as gender and smile from a single face image represented by deep features. We pose the problem of apparent age estimation as an instance of the multi-class structured output SVM classifier followed by a softmax expected value refinement. The gender and smile predictions are treated as binary classification problems. The proposed solution first detects the face in the image and then extracts deep features from the cropped image around the detected face. We use a convolutional neural network with VGG-16 architecture [25] for learning deep features. The network is pretrained on the ImageNet [24] database and then fine-tuned on IMDB-WIKI [21] and ChaLearn 2015 LAP datasets [8]. We validate our methods on the ChaLearn 2016 LAP dataset [9]. Our structured output SVMs are trained solely on ChaLearn 2016 LAP data. We achieve excellent results for both apparent age prediction and gender and smile classification.Uricar M., Timofte R., Rothe R., Matas J., Van Gool L., ''Structured output SVM prediction of apparent age, gender and smile from deep features'', 29th IEEE conference on computer vision and pattern recognition workshops - CVPRW 2016, pp. 25-33, June 26 - July 1, 2016, Las Vegas, Nevada, USA.status: publishe