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Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection...
Faster R-CNN (R corresponds to “Region”) which combined the RPN network and the Fast R-CNN network is one of the best ways to object detection of R-CNN series based on deep learning. The proposal obtained by RPN is directly connected to the ROI Pooling layer, which is a framework for CNN to achieve end-to-end object detection. The feasibility of Faster R-CNN implementation of ResNet101 network and...
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In...
Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the initial training data. They suffer from “catastrophic forgetting”–an abrupt degradation of performance on the original set of classes, when the training...
High quality teaching has always been the pursuit of universities, but the automatic and real time evaluation on the quality of classroom teaching has not been achieved. To solve this problem, a real-time processing of classroom video through face detection technology and a software system to provide a basis for judging the quality of teaching is proposed in this paper. The main application of machine...
Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and...
This paper presents a comparison between the performances of LBP and Haar features in a Cascade Classifier, which aims automatically to count immature whiteflies (nymphs). The relevance of counting these nymphs is related to pest management, which leads to an increase in food production. Evaluate differences in weak classifiers can lead to better understanding and implementation of object detection...
In this paper, we propose a multi-modal search engine for interior design that combines visual and textual queries. The goal of our engine is to retrieve interior objects, e.g. furniture or wall clocks, that share visual and aesthetic similarities with the query. Our search engine allows the user to take a photo of a room and retrieve with a high recall a list of items identical or visually similar...
Ultrasonic Non-Destructive Evaluation (NDE) uses high frequency acoustic waves to evaluate materials, and often signal processing is required to detect echoes from defects in the presence of micro-structure scattering noise. Scattering noise is known as the clutter. The clutter interferes with the flaw signal and cannot be completely separated from it by using conventional signal processing methods...
In the present paper, we propose a deep network architecture in order to improve the accuracy of pedestrian detection. The proposed method contains a proposal network and a classification network that are trained separately. We use a single shot multibox detector (SSD) as a proposal network to generate the set of pedestrian proposals. The proposal network is fine-tuned from a pre-trained network by...
We present an optimization technique for general object detection and an algorithm for training decision trees. By delaying the calculation of the features as late as possible we drastically reduce the execution time. At detection we alternate between evaluating the necessary features and eliminating candidates. This enables us to have both a rich pool of features and a powerful classifier while keeping...
Boosted forest (BF) is a commonly used method for object detection. With the help of cascade strategy, it can efficiently reject non-object windows and finally, combined with sliding window paradigm, give the locations of target objects in an image. In the literature, many aspects of cascaded boosted forest (CBF) have been well studied, such as image representation, tree split and cascade structure...
Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real...
In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections. However, the size of network input is limited by the amount of memory available on GPUs. Moreover, performance degrades when detecting small objects. To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small...
The object detection is a challenging problem in computer vision with various potential real-world applications. The objective of this study is to evaluate the deep learning based object detection techniques for detecting drones. In this paper, we have conducted experiments with different Convolutional Neural Network (CNN) based network architectures namely Zeiler and Fergus (ZF), Visual Geometry...
Faster R-CNN has established itself as the de-facto best object detector but it remains strongly limited in two aspects: (i) it is sensitive to background clutter and its classification performance decreases when it is confronted with more noisy proposals; (ii) it suffers when the objects vary largely in scale and specifically for the small objects. We address both issues with our geometric-proposals...
With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object...
Different types of vehicles, such as buses and cars, can be quite different in shapes and details. This makes it more difficult to try to learn a single feature vector that can detect all types of vehicles using a single object class. We proposed an approach to perform vehicle detection with Sub-Classes categories learning using R-CNN in order to improve the performance of vehicle detection. Instead...
On-road obstacle detection and classification is one of the key tasks in the perception system of self-driving vehicles. Since vehicle tracking involves localizationand association of vehicles between frames, detection and classification of vehicles is necessary. Vision-based approaches are popular for this task due to cost-effectiveness and usefulness of appearance information associated with the...
With the development of intelligent device and social media, the data bulk on Internet has grown with high speed. As an important aspect of image processing, object detection has become one of the international popular research fields. In recent years, the powerful ability with feature learning and transfer learning of Convolutional Neural Network (CNN) has received growing interest within the computer...
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