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This paper gives a brief review of few common path tracking techniques used in the design of autonomous vehicles. Technique such as pure-pursuit, vector pursuit as well as CFpursuit which are all based on the pure-pursuit techniques were discussed and a detailed comparism was made between these geometric techniques. Also this review work discusses areas were little research has been done. Areas such as tracking of an implicit part of a mobile robot and proposes an area where feature research can be done such as tracking of both implicit and explicit path for a non-holonomic mobile robot.

Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is suitable for real-time applications with low latency.

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With the increase in the number of deaf-mute people in the Arab world and the lack of Arabic sign language (ArSL) recognition benchmark data sets, there is a pressing need for publishing a large-volume and realistic ArSL data set. This study presents such a data set, which consists of 150 isolated ArSL signs. The data set is challenging due to the great similarity among hand shapes and motions in the collected signs. Along with the data set, a sign language recognition algorithm is presented. The authors' proposed method consists of three major stages: hand segmentation, hand shape sequence and body motion description, and sign classification. The hand shape segmentation is based on the depth and position of the hand joints. Histograms of oriented gradients and principal component analysis are applied on the segmented hand shapes to obtain the hand shape sequence descriptor. The covariance of the three-dimensional joints of the upper half of the skeleton in addition to the hand states and face properties are adopted for motion sequence description. The canonical correlation analysis and random forest classifiers are used for classification. The achieved accuracy is 55.57% over 150 ArSL signs, which is considered promising.

Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as 'unknown', because the system cannot recognize the input of the new pattern.

Fingerprint presentation attack detection (FPAD) is becoming an increasingly challenging problem due to the continuous advancement of attack techniques, which generate "realistic-looking" fake fingerprint presentations. Recently, laser speckle contrast imaging (LSCI) has been introduced as a new sensing modality for FPAD. LSCI has the interesting characteristic of capturing the blood flow under the skin surface. Toward studying the importance and effectiveness of LSCI for FPAD, we conduct a comprehensive study using different patch-based deep neural network architectures. Our studied architectures include 2D and 3D convolutional networks as well as a recurrent network using long short-term memory (LSTM) units. The study demonstrates that strong FPAD performance can be achieved using LSCI. We evaluate the different models over a new large dataset. The dataset consists of 3743 bona fide samples, collected from 335 unique subjects, and 218 presentation attack samples, including six different types of attacks. To examine the effect of changing the training and testing sets, we conduct a 3-fold cross validation evaluation. To examine the effect of the presence of an unseen attack, we apply a leave-oneattack out strategy. The FPAD classification results of the networks, which are separately optimized and tuned for the temporal and spatial patch-sizes, indicate that the best performance is achieved by LSTM.