arXiv subjects

Wassim Bouachir

Publications and source records attributed to Wassim Bouachir.

25 records · Page 2Linked to original sources

arXiv2021

Multiple Convolutional Features in Siamese Networks for Object Tracking

Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem, and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks. The source code and trained models are available at https://github.com/zhenxili96/MFST.

cs.CV↗

arXiv2020

Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters

During the last years, deep learning trackers achieved stimulating results while bringing interesting ideas to solve the tracking problem. This progress is mainly due to the use of learned deep features obtained by training deep convolutional neural networks (CNNs) on large image databases. But since CNNs were originally developed for image classification, appearance modeling provided by their deep layers might be not enough discriminative for the tracking task. In fact,such features represent high-level information, that is more related to object category than to a specific instance of the object. Motivated by this observation, and by the fact that discriminative correlation filters(DCFs) may provide a complimentary low-level information, we presenta novel tracking algorithm taking advantage of both approaches. We formulate the tracking task as a two-stage procedure. First, we exploit the generalization ability of deep features to coarsely estimate target translation, while ensuring invariance to appearance change. Then, we capitalize on the discriminative power of correlation filters to precisely localize the tracked object. Furthermore, we designed an update control mechanism to learn appearance change while avoiding model drift. We evaluated the proposed tracker on object tracking benchmarks. Experimental results show the robustness of our algorithm, which performs favorably against CNN and DCF-based trackers. Code is available at: https://github.com/AhmedZgaren/Coarse-to-fine-Tracker

cs.CV↗

arXiv2020

Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait

Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians. In this context, the use of an intelligent gait analysis algorithm may assist physicians in order to facilitate the diagnosis process. This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information. We used 1D convolutional neural network (1D-Convnet) to build a Deep Neural Network (DNN) classifier. The proposed model processes 18 1D-signals coming from foot sensors measuring the vertical ground reaction force (VGRF). The first part of the network consists of 18 parallel 1D-Convnet corresponding to system inputs. The second part is a fully connected network that connects the concatenated outputs of the 1D-Convnets to obtain a final classification. We tested our algorithm in Parkinson's detection and in the prediction of the severity of the disease with the Unified Parkinson's Disease Rating Scale (UPDRS). Our experiments demonstrate the high efficiency of the proposed method in the detection of Parkinson disease based on gait data. The proposed algorithm achieved an accuracy of 98.7 %. To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition. Furthermore, we achieved an accuracy of 85.3 % in Parkinson's severity prediction. To the best of our knowledge, this is the first algorithm to perform a severity prediction based on the UPDRS. Our results show that the model is able to learn intrinsic characteristics from gait data and to generalize to unseen subjects, which could be helpful in a clinical diagnosis.

cs.LG↗

arXiv2018

Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches

The emergence of RGB-D sensors offered new possibilities for addressing complex artificial vision problems efficiently. Human posture recognition is among these computer vision problems, with a wide range of applications such as ambient assisted living and intelligent health care systems. In this context, our paper presents novel methods and ideas to design automatic posture recognition systems using an RGB-D camera. More specifically, we introduce two supervised methods to learn and recognize human postures using the main types of visual data provided by an RGB-D camera. The first method is based on convolutional features extracted from 2D images. Convolutional Neural Networks (CNNs) are trained to recognize human postures using transfer learning on RGB and depth images. Secondly, we propose to model the posture using the body joint configuration in the 3D space. Posture recognition is then performed through SVM classification of 3D skeleton-based features. To evaluate the proposed methods, we created a challenging posture recognition dataset with a considerable variability regarding the acquisition conditions. The experimental results demonstrated comparable performances and high precision for both methods in recognizing human postures, with a slight superiority for the CNN-based method when applied on depth images. Moreover, the two approaches demonstrated a high robustness to several perturbation factors, such as scale and orientation change.

cs.CV↗

arXiv2018

Multi-Branch Siamese Networks with Online Selection for Object Tracking

In this paper, we propose a robust object tracking algorithm based on a branch selection mechanism to choose the most efficient object representations from multi-branch siamese networks. While most deep learning trackers use a single CNN for target representation, the proposed Multi-Branch Siamese Tracker (MBST) employs multiple branches of CNNs pre-trained for different tasks, and used for various target representations in our tracking method. With our branch selection mechanism, the appropriate CNN branch is selected depending on the target characteristics in an online manner. By using the most adequate target representation with respect to the tracked object, our method achieves real-time tracking, while obtaining improved performance compared to standard Siamese network trackers on object tracking benchmarks.

cs.CV↗

arXiv2015

Reproducible Evaluation of Pan-Tilt-Zoom Tracking

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. However, it is very difficult to assess the progress that has been made on this topic because there is no standard evaluation methodology. The difficulty in evaluating PTZ tracking algorithms arises from their dynamic nature. In contrast to other forms of tracking, PTZ tracking involves both locating the target in the image and controlling the motors of the camera to aim it so that the target stays in its field of view. This type of tracking can only be performed online. In this paper, we propose a new evaluation framework based on a virtual PTZ camera. With this framework, tracking scenarios do not change for each experiment and we are able to replicate online PTZ camera control and behavior including camera positioning delays, tracker processing delays, and numerical zoom. We tested our evaluation framework with the Camshift tracker to show its viability and to establish baseline results.

cs.CV↗

arXiv2013

A Bag of Words Approach for Semantic Segmentation of Monitored Scenes

This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera. Our algorithm classifies each perceptually homogenous region as one of the predefined classes learned from a collection of manually labelled images. The proposed approach combines two different types of information. First, color segmentation is performed to divide the scene into perceptually similar regions. Then, the second step is based on SIFT keypoints and uses the bag of words representation of the regions for the classification. The prediction is done using a Naïve Bayesian Network as a generative classifier. Compared to existing techniques, our method provides more compact representations of scene contents and the segmentation result is more consistent with human perception due to the combination of the color information with the image keypoints. The experiments conducted on a publicly available data set demonstrate the validity of the proposed method.

cs.CV↗