Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we propose an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.Comment: Submitted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

DESIGN A SECURITY FIREWALL POLICY TO FILTER INCOMING TRAFFIC IN PACKET SWITCHED NETWORKS USING CLASSIFICATION METHODS

Firewalls are core elements in network security. However, managing firewall rules, especially for enterprise networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires to overcome and filter a range of special attacks or issues in network. In this paper, we present a machine learning based algorithm that filter Denial of Service (DoS) attacks in networks. This filtering algorithm has been designed by using a classification algorithm based on principal component and correlation based filters. We show good quality and performance of our algorithm experimentally by executing our algorithm on a several packet flow data sets.Firewalls are core elements in network security. However, managing firewall rules, especially for enterprise networks, has become complex and error-prone. Firewall filtering rules have to be carefully written and organized in order to correctly implement the security policy. In addition, inserting or modifying a filtering rule requires to overcome and filter a range of special attacks or issues in network. In this paper, we present a machine learning based algorithm that filter Denial of Service (DoS) attacks in networks. This filtering algorithm has been designed by using a classification algorithm based on principal component and correlation based filters. We show good quality and performance of our algorithm experimentally by executing our algorithm on a several packet flow data sets

Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks

Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide. With the arrival of Self-driving cars it has become a staple challenge to solve the automatic recognition of Traffic and Hand-held signs in the major streets. Various machine learning techniques like Random Forest, SVM as well as deep learning models has been proposed for classifying traffic signs. Though they reach state-of-the-art performance on a particular data-set, but fall short of tackling multiple Traffic Sign Recognition benchmarks. In this paper, we propose a novel and one-for-all architecture that aces multiple benchmarks with better overall score than the state-of-the-art architectures. Our model is made of residual convolutional blocks with hierarchical dilated skip connections joined in steps. With this we score 99.33% Accuracy in German sign recognition benchmark and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover, we propose a newly devised dilated residual learning representation technique which is very low in both memory and computational complexity

An Efficient Approach for Using Expectation Maximization Algorithm in Capsule Networks

Capsule Networks (CapsNets) are brand-new architectures that have shown ground-breaking results in certain areas of Computer Vision (CV). In 2017, Hinton and his team introduced CapsNets with routing-by-agreement in "Sabour et al" and in a more recent paper "Matrix Capsules with EM Routing" they proposed a more complete architecture with Expectation-Maximization (EM) algorithm. Unlike the traditional convolutional neural networks (CNNs), this architecture is able to preserve the pose of the objects in the picture. Due to this characteristic, it has been able to beat the previous state-of-theart results on the smallNORB dataset, which includes samples with various view points. Also, this architecture is more robust to white box adversarial attacks. However, CapsNets have two major drawbacks. They can't perform as well as CNNs on complex datasets and, they need a huge amount of time for training. We try to mitigate these shortcomings by finding optimum settings of EM routing iterations for training CapsNets. Unlike the past studies, we use un-equal numbers of EM routing iterations for different stages of the CapsNet. For our research, we use three datasets: Yale face dataset, Belgium Traffic Sign dataset, and Fashion-MNIST dataset

Traffic sign detection for U.S. roads:Remaining challenges and a case for tracking

Abstract — Traffic sign detection is crucial in intelligent vehi-cles, no matter if one’s objective is to develop Advanced Driver Assistance Systems or autonomous cars. Recent advances in traffic sign detection, especially the great effort put into the competition German Traffic Sign Detection Benchmark, have given rise to very reliable detection systems when tested on European signs. The U.S., however, has a rather different approach to traffic sign design. This paper evaluates whether a current state-of-the-art traffic sign detector is useful for American signs. We find that for colorful, distinctively shaped signs, Integral Channel Features work well, but it fails on the large superclass of speed limit signs and similar designs. We also introduce an extension to the largest public dataset of American signs, the LISA Traffic Sign Dataset, and present an evaluation of tracking in the context of sign detection. We show that tracking essentially suppresses all false positives in our test set, and argue that in order to be useful for higher level analysis, any traffic sign detection system should contain tracking