Gait and body movement are a window to human brain which make these activities unique for each person. These activities are used to diagnose some disorders related to parts of brain which causes have not been known such as Autism Disorders (AD). The traditional diagnostic methods of AD are time-consuming and highly dependent on clinician’s judgment which is based on behaviour assessment. This approach leads to subjective interpretations that differ from doctor to another and affect by strengths and weaknesses of patient. Therefore this paper aims to diagnosis of AD based on gait and body movement analysis. At first, Kinect v2 uses to create a 3D dataset, which includes three dimensional joints positions, joints trajectories video, skeleton movement video captured by Kinect v2, and color videos captured by Samsung Note 9 camera. This paper also aims to classify children with autism from normal children by proposed system based on four stages: Augmentation of the database by using seven transformations to deal with small number of autism cases; Extracting features that we think play an important role in classification; Reducing data dimensions using Principal Component Analysis; using Rough Set to classify dataset. Results show that classification is 92% after augmentation.

Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.

-This paper presents a new approach for hiding the secret image inside another image file, depending on the signature of coefficients. The proposed system consists of two general stages. The first one is the hiding stage which consist of the following steps (Read the cover image and message image, Block collections using the chain code and similarity measure, Apply DCT Transform, Signature of coefficients, Hiding algorithm , Save information of block in boundary, Reconstruct block to stego image and checking process). The second stage is extraction stage which consist of the following steps ( read the stego image, Extract information of block from boundary, Block collection, Apply DCT transform, Extract bits of message and save it to buffer, Extracting message).

<p>The development of tracking and surveillance devices makes extracting useful information efficiently. Head tracking is an efficient method to obtain then analyze trajectory data and make a decision based on the spatiotemporal information of videos. Many applications are based on head tracking such as diseases some diagnosis,  the gestures languages, and drowsiness detection and so on. Abnormal head movement detection can be achieved using spatial information based on a single image (one frame) at a time without considering the temporal information over time. In this paper, a new method based on multi-images is proposed to track head in order to detect abnormal head movement depending on spatiotemporal Feature using Deep Neural Network DNN that employed the 3-Dimensional Convolution Neural Networks 3D CNN. The proposed method extracts the spatial information as well as the temporal information available in a video then analysis this information to make the decision based on time series (sequences of frames); these time series provides the tracking to the head overtime to make the decision. The new dataset created and gathered to implement with the proposed system and called Normal Abnormal Head Movement Dataset (NAHM) video dataset. The new dataset provides different subjects with different conditions that give more efficiency in the implementation of the proposed system. The accuracy of the training set that achieves by the proposed system reach to 88% and of validation set reaches to 86%. The values of loss function reach to 0.3 for the training set and 0.4 for the validation set.</p>

In this paper, we propose an experimental study of multi-camera collaborative network for surveillance the highway traffic turn in real life scenario, the target of surveillance based offline video processing is capturing the violated vehicles that driving violated paths in many cases specified by user-defined rules. Best topology of the experiment zone is considered and covered by four pillars; each has two (fixed and motorized) cameras that casing the entire specific effective field of view. As to author knowledge, there is no such available experiment, and hence, it could serve researchers that interested in. However, the experiment is done for around 180 recorded hours for 8 videos during 9 days, each video for one camera. It is designed based on the collaborative cameras principle for intelligent video surveillance systems and the outcomes show that the surveillance and the tracking of violated vehicles have been successes in most user-defined rules cases for more than 90 % cases.