Evidential combination of pedestrian detectors

International audienceThe importance of pedestrian detection in many applications has led to the development of many algorithms. In this paper, we address the problem of combining the outputs of several detectors. A pre-trained pedestrian detector is seen as a black box returning a set of bounding boxes with associated scores. A calibration step is first conducted to transform those scores into a probability measure. The bounding boxes are then grouped into clusters and their scores are combined. Different combination strategies using the theory of belief functions are proposed and compared to probabilistic ones. A combination rule based on triangular norms is used to deal with dependencies among detectors. More than 30 state-of-the-art detectors were combined and tested on the Caltech Pedestrian Detection Benchmark. The best combination strategy outperforms the currently best performing detector by 9% in terms of log-average miss rate

Sensor fusion methodology for vehicle detection

A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriage-ways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems

Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework

International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

Vehicle recognition and tracking using a generic multi-sensor and multi-algorithm fusion approach

International audienceThis paper tackles the problem of improving the robustness of vehicle detection for Adaptive Cruise Control (ACC) applications. Our approach is based on a multisensor and a multialgorithms data fusion for vehicle detection and recognition. Our architecture combines two sensors: a frontal camera and a laser scanner. The improvement of the robustness stems from two aspects. First, we addressed the vision-based detection by developing an original approach based on fine gradient analysis, enhanced with a genetic AdaBoost-based algorithm for vehicle recognition. Then, we use the theory of evidence as a fusion framework to combine confidence levels delivered by the algorithms in order to improve the classification 'vehicle versus non-vehicle'. The final architecture of the system is very modular, generic and flexible in that it could be used for other detection applications or using other sensors or algorithms providing the same outputs. The system was successfully implemented on a prototype vehicle and was evaluated under real conditions and over various multisensor databases and various test scenarios, illustrating very good performances

Augmenting Deep Learning Performance in an Evidential Multiple Classifier System

International audienceThe main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision