The moving object classification is a crucial step for several video surveillance applications whatever in the visible or thermal spectra. It still remains an active field of research considering the diversity of challenges related to this topic mainly in the context of an outdoor scene. In order to overcome several intricate situations, many moving objects classification methods have been proposed in the literature. Particular interest is given to the classes “Pedestrian” and “Vehicle”. In this paper, we have proposed a moving object classification approach based on deep learning methods from visible and infrared spectra. Three series of experiments carried on the challenging dataset “CD.net 2014” have proved that the proposed method reach accurate moving objects classification results when compared to methods based on deep learning and handcrafted features.

The age verification is an important task in various context of applications like access control in spaces in hotels which are prohibited for children and teenagers, in dangerous spaces for children and in public area during a spread of virus among others. In fact, the age verification consists in classifying the face images into different age groups while dealing with the face appearance variation affected by occlusion, pose variation, low resolution, scale variation and illumination variation. This work introduced an access control application based on the age verification in an uncontrolled environment. In fact, we proposed a new two-level age classification method based on deep learning in order to classify the face images into eight age groups. Actually, the two-level classification strategy help reducing the confusion between the inter and intra age groups. Our experiments were performed on the multi-constrained Adience benchmark. The obtained results illustrate the effectiveness and robustness of the proposed age classification method in an uncontrolled environment.

Human action recognition has received a lot of attention in computer vision community given its interest in many real applications. In this paper, we proposed a new method for human action recognition based on deep learning methods. The main contribution of the proposed method is an efficient combination of two Convolutional neural networks. The two-stream framework allows to fully utilize the rich multimodal information in videos. In fact, we explored the complementarity between appearance information and motion information to represent human actions. Specifically, we suggested a spatial Convolutional Neural Network performed on still individual images to model spatial information. To exploit motion between frames, a second Convolutional Neural Network is processed on accumulated optical flow images obtained by stacking the optical flow estimations between consecutive frames in a single image. Then, a fusion score is performed between the two Convolutional Neural Networks to achieve the appropriate class. In order to prove the performance of our method, we trained and evaluated our architecture on a standard human actions benchmark, the Weizmann dataset.

Person re-identification presents an active research area for intelligent video surveillance systems. The purpose is to find the same person from disjoint camera views at different times and locations. In this paper, we propose a novel Bi-Model Person Re-identification method (BiMPeR) that combines the appearance and the gait features to improve the re-identification performance and to handle the problem of similar appearances. The main idea is to prove the complementarity of these two modalities to extract a discriminative person signature for the re-identification problem. A score fusion method was adopted to combine these two modalities to reflect the impact of each one on the final decision. Experiments were performed on the CASIA-B database revealing promising results and showing the effectiveness of the proposed method against state-of-the-art uni-model methods.

People search based on semantic attributes presents an important task for several surveillance applications. The aim is to locate a suspect or to find a missing person in public areas based on an appearance description provided by a witness. In this paper, we propose a novel people search method based on an appearance description provided in terms of semantic attributes under uncontrolled acquisition conditions (e.g. gender, worn bags, carried objects, and clothes pattern). To this end, we propose to learn a set of deep semantic attribute classifiers based on the convolutional neural network. Experimental evaluations, based on the confusion matrix and the statistical tests, prove the efficiency of our method compared to state-of-the-art CNN architectures. Further, a comparison with state-of-the-art attribute classification methods is also conducted and confirms the efficiency of our method.

Gait has become a popular trait for biometric person recognition/re-identification. This is due to its advantage of being captured without any subject cooperation. This made it suitable especially for video surveillance applications. However, the gait features obtained in such scenarios depends on the observed walking direction of the subject. In this paper, we deal with the problem related to walking direction estimation in unconstrained environments. Covariates factors (i.e. carrying different types of bag, clothing) affect considerably the accuracy of walking direction estimation problem. Therefore, we have proposed a solution which is suitable for both real time application and unconstrained environment where the user walking direction is different and affected by covariates factors. The discriminative power of this solution is verified through experiments. The performance of this method was evaluated on the CASIA-B database. Experimental results prove the effectiveness of our proposed walking direction estimation method.

Person re-identification consists in recognizing the same person across non-overlapping camera views at different times and locations. It presents an important yet challenging task for intelligent video surveillance systems due to the large variations of pose, viewpoint, lighting and occlusion between the different camera views. Although a variety of algorithms have been presented in the past few years, most of them usually assume a pre-cropped bounding box of fully visible people taken from two different cameras to perform the re-identification process. However, in real-world video surveillance systems several challenges need to be addressed such as re-identifying truncated people and alleviating the hash lighting variation of the different camera views. In this paper, we propose an adaptive person re-identification approach that re-identifies a person irrespective of his status, i.e. truncated or fully visible, based on the apparent salient body regions driven by their robust appearance characteristics in a large camera network. The proposed approach has been experimentally validated on the High Definition Analytics (HDA) and viewpoint invariant perdestrian recognition data sets which include several re-identification challenges. The outcomes of this evaluation show promising results and demonstrate the effectiveness of our approach compared to other state-of-the-art approaches.

This paper introduces a novel method of moving object classification in Infrared and Visible spectra. This method is based on a data-mining process by combining a set of best features based on shape, texture and motion. The proposed method relies either on visible spectrum or on infrared spectrum according to weather conditions (sunny days, rain, fog, snow, etc.) and timing of the video acquisition. Experimental studies are carried out to prove the efficiency of our predictive models to classify moving objects and the originality of our process with intelligent fusion of VIS-IR spectra.