An Attentionbased Recurrent Convolutional Network for Vehicle Taillight Recognition Vehicle taillight recognition is an important application for automated driving, especially for intent prediction of ado vehicles and trajectory planning of the ego vehicle. In this work, we propose an endtoend deep learning framework to recognize taillights, i.e. rear turn and brake signals, from a sequence of images. The proposed method starts with a Convolutional Neural Network (CNN) to extract spatial features, and then applies a Long ShortTerm Memory network (LSTM) to learn temporal dependencies. Furthermore, we integrate attention models in both spatial and temporal domains, where the attention models learn to selectively focus on both spatial and temporal features. Our method is able to outperform the state of the art in terms of accuracy on the UC Merced Vehicle Rear Signal Dataset, demonstrating the effectiveness of attention models for vehicle taillight recognition.

OCR OntheGo: Robust EndtoEnd Systems for Reading License Plates & Street Signs We work on the problem of recognizing license plates and street signs automatically in challenging conditions such as chaotic traffic. We leverage stateoftheart text spotters to generate a large amount of noisy labeled training data. The data is filtered using a pattern derived from domain knowledge. We augment training and testing data with interpolated boxes and annotations that makes our training and testing robust. We further use synthetic data during training to increase the coverage of the training data. We train two different models for recognition. Our baseline is a conventional Convolution Neural Network (CNN) encoder followed by a Recurrent Neural Network (RNN) decoder. As our first contribution, we bypass the detection phase by augmenting the baseline with an Attention mechanism in the RNN decoder. Next, we build in the capability of training the model endtoend on scenes containing license plates by incorporating inception based CNN encoder that makes the model robust to multiple scales. We achieve improvements of 3.75% at the sequence level, over the baseline model. We present the first results of using multiheaded attention models on text recognition in images and illustrate the advantages of using multipleheads over a single head. We observe gains as large as 7.18% by incorporating multiheaded attention. We also experiment with multiheaded attention models on French Street Name Signs dataset (FSNS) and a new Indian Street dataset that we release for experiments. We observe that such models with multiple attention masks perform better than the model with singleheaded attention on three different datasets with varying complexities. Our models outperform stateoftheart methods on FSNS and IIITILST Devanagari datasets by 1.1% and 8.19% respectively.