Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps

Offline handwriting recognition using Artificial Neural Network and Hidden Markov Model

Cursive handwriting is the most natural way for humans to communicate and record information. The developments of automatic systems that are capable of recognizing human handwritings offer a new way of improving human-computer interface and of enabling computers to perform repetitive tasks of reading and processing handwritten documents more efficiently. The aim of this thesis is to design an offline handwritten word recognition system based on the hybrid of Artificial Neural Network (ANN) and Hidden Markov Model (HMM). The Input space segmentation (INSEG) approach proposes various ways to segment word into characters. This approach creates the problem of junks - character hypotheses that are not true characters. Two training approaches have been introduced, namely character level discriminant training and word-level discriminant training. The latter shows integration of the ANN and HMM by using the gradient descent algorithm. Different topologies of the ANN have been investigated for modeling of junks. Three isolated word databases, namely, IRONOFF, AWS and SRTP, have been used as the evaluation of the proposed system. Experimental results have shown that the ANN-HMM hybrid with word-level discriminant training consistently yield better recognition accuracy compared to character level discriminant training and discrete HMM-based recognition system. It achieves recognition accuracy of 97.3%, 88.4%, 90.5% and 95.8%, on IRONOFF-1 96, IRONOFF-1 991, SRTP-Cheque, and AWS, respectively

Invariant object recognition using circular pairwise convolutional networks

Invariant object recognition (IOR) has been one of the most active research areas in computer vision. However, there is no technique able to achieve the best performance in all possible domains. Out of many techniques, convolutional network (CN) is proven to be a good candidate in this area. Given large numbers of training samples of objects under various variation aspects such as lighting, pose, background, etc., convolutional network can learn to extract invariant features by itself. This comes with the price of lengthy training time. Hence, we propose a circular pairwise classification technique to shorten the training time. We compared the recognition accuracy and training time complexity between our approach and a benchmark generic object recognizer LeNet7 which is a monolithic convolutional network