Abstract Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2 of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https: github.com cs-chan Exclusively-Dark-Image-Dataset .
Thermal and near infrared cameras are generally used to counter limited light for surveillance operations at night. Common object detection is not usually addressed in surveillance works, although the closest would be face recognition ( @cite_3 @cite_32 ) and pedestrian detection ( @cite_48 @cite_51 @cite_8 @cite_40 @cite_45 ). Datasets such as the OTCVBS ( @cite_48 @cite_13 @cite_3 @cite_49 ), LSI ( @cite_50 ), and LDHF ( @cite_32 ) were acquired with careful setup and sophisticated hardware that is much more difficult to achieve compared to visible light images taken by common digital cameras. Moreover, the unrealistic images provided are not suitable for the practical understanding of low-light in common vision.
@cite_13 @cite_8 @cite_48 @cite_32 @cite_3 @cite_40 @cite_45 @cite_49 @cite_50 @cite_51
2015148749 2051071899 2120339771 2010586245 2126074213 2150764772 2000814733 2039094769 2140743285
We present a new background-subtraction technique fusing contours from thermal and visible imagery for persistent object detection in urban settings. Statistical background-subtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regions-of-interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then fused into a single image. An A^* path-constrained search along watershed boundaries of the regions-of-interest is used to complete and close any broken segments in the fused contour image. Lastly, the contour image is flood-filled to produce silhouettes. Results of our approach are evaluated quantitatively and compared with other low- and high-level fusion techniques using manually segmented data. The interest in automatic surveillance and monitoring systems has been growing over the last years due to increasing demands for security and law enforcement applications. Although, automatic surveillance systems have reached a significant level of maturity with some practical success, it still remains a challenging problem due to large variation in illumination conditions. Recognition based only on the visual spectrum remains limited in uncontrolled operating environments such as outdoor situations and low illumination conditions. In the last years, as a result of the development of low-cost infrared cameras, night vision systems have gained more and more interest, making infrared (IR) imagery as a viable alternative to visible imaging in the search for a robust and practical identification system. Recently, some researchers have proposed the fusion of data recorded by an IR sensor and a visible camera in order to produce information otherwise not obtainable by viewing the sensor outputs separately. In this article, we propose the application of finite mixtures of multidimensional asymmetric generalized Gaussian distributions for different challenging tasks involving IR images. The advantage of the considered model is that it has the required flexibility to fit different shapes of observed non-Gaussian and asymmetric data. In particular, we present a highly efficient expectation-maximization (EM) algorithm, based on minimum message length (MML) formulation, for the unsupervised learning of the proposed model's parameters. In addition, we study its performance in two interesting applications namely pedestrian detection and multiple target tracking. Furthermore, we examine whether fusion of visual and thermal images can increase the overall performance of surveillance systems. We present a two-stage template-based method to detect people in widely varying thermal imagery. The approach initially performs a fast screening procedure using a generalized template to locate potential person locations. Next an AdaBoosted ensemble classifier using automatically tuned filters is employed to test the hypothesized person locations. We demonstrate and evaluate the approach using a challenging dataset of thermal imagery Abstract Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching. Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups Pedestrian detection plays a paramount role in advanced driver assistant system (ADAS) and autonomous vehicles, especially with the growth of aging population. The purpose of pedestrian detection is to identify and locate people in a dynamic scene or environment. It needs to tackle the challenges such as illumination, color, texture, clothing, and background complexities. Different from visible imaging system, thermal imaging depends on objects' emissivity, and thus has the advantage on discriminating human body from the cool background. In this study, sparse representation is proposed for pedestrian detection in thermal images. Two types of dictionaries, i.e. a generic dictionary optimized by K-SVD and a naive dictionary with basis atoms being directly composed of training samples, are employed to represent image features. In the implementation, a boundary box shrinking scheme is applied to improve the accuracy of the detection through finding proper size for the boundary box. The experimental results demonstrate a comparable performance of the proposed approach. In this paper, a robust approach using a shape distribution histogram (SDH) feature and modified sparse representation classification (MSRC) for pedestrian detection in thermal infrared imagery is proposed. In this framework, the candidate regions that are more likely to contain the pedestrians are first detected based on the Contour Saliency Map. Then distances between random points on the thinned contour map of objects in the candidate regions are applied to acquire the SDH feature. SDH is a robust and discriminative feature which can precisely describe the pedestrian characteristics. Finally, a robust MSRC classifier which has high accuracy is used to recognize the true pedestrians. Experiments are conducted over the OSU thermal pedestrian database by comparing with other algorithms. The proposed method shows an excellent performance in detecting pedestrians. HighlightsRobust pedestrian detection using the SDH feature and MSRC classifier in infrared imagery.Discriminative SDH feature can precisely describe the pedestrian characteristic.Robust MSRC classifier is used to classify the pedestrians and non-pedestrians. Abstract When dealing with the registration of information from different image sources, the de facto similarity measure used is Mutual Information (MI). Although MI gives good performance in many image registration applications, recent works in thermal–visible registration have shown that other similarity measures can give results that are as accurate, if not more than MI. Furthermore, some of these measures also have the advantage of being calculated independently from each image to register, which allows them to be integrated more easily in energy minimization frameworks. In this article, we investigate the accuracy of similarity measures for thermal–visible image registration of human silhouettes, including MI, Sum of Squared Differences (SSD), Normalized Cross-Correlation (NCC), Histograms of Oriented Gradients (HOG), Local Self-Similarity (LSS), Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Census, Fast Retina Keypoint (FREAK), and Binary Robust Independent Elementary Feature (BRIEF). We tested the various similarity measures in dense stereo matching tasks over 25,000 windows to have statistically significant results. To do so, we created a new dataset in which one to five humans are walking in a scene in various depth planes. Results show that even if MI is a very strong performer, particularly for large regions of interest (ROI), LSS gives better accuracies when ROI are small or segmented into small fragments because of its ability to capture shape. The other tested similarity measures did not give consistently accurate results. This paper presents a novel nighttime pedestrian detection approach only using a near infrared camera, which can be used in a practical driver assistance systems. This method can be divided into three steps: selection step, preprocess step and recognition step. Firstly, objects in the video are separated with an adaptive dual thresholds segmentation method in the selection step; Secondly, most of non-pedestrians are discarded with some constraints in the preprocess step; Finally, in the recognition step a cascaded classifiers with Histograms of Oriented Gradients and Adaptive Boosting Algorithm are introduced. Experiments on video sequences show that the proposed pedestrian detection approach has a high detection rate as well as a very low false alarm rate and run in real-time.