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
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ABSTRACT In low light conditions, visible light face identication is infeasible due to the lack of illumination. For nighttimesurveillance, thermal imaging is commonly used because of the intrinsic emissivity of thermal radiation from thehuman body. However, matching thermal images of faces acquired at nighttime to the predominantly visiblelight face imagery in existing government databases and watch lists is a challenging task. The diculty arisesfrom the signicant dierence between the faces thermal signature and its visible signature (i.e. the modalitygap). To match the thermal face to the visible face acquired by the two dierent modalities, we applied facerecognition algorithms that reduce the modality gap in each step of face identication, from low-level analysis tomachine learning techniques. Specically, partial least squares-discriminant analysis (PLS-DA) based approacheswere used to correlate the thermal face signatures to the visible face signatures, yielding a thermal-to-visible faceidentication rate of 49.9 . While this work makes progress for thermal-to-visible face recognition, more eortsneed to be devoted to solving this dicult task. Successful development of a thermal-to-visible face recognitionsystem would signicantly enhance the Nations nighttime surveillance capabilities.Keywords: thermal, visible, thermal-to-visible face recognition, face, recognition, multi, modal 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. 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. Compared with visible object tracking, thermal infrared (TIR) object tracking can track an arbitrary target in total darkness since it cannot be influenced by illumination variations. However, there are many unwanted attributes that constrain the potentials of TIR tracking, such as the absence of visual color patterns and low resolutions. Recently, structured output support vector machine (SOSVM) and discriminative correlation filter (DCF) have been successfully applied to visible object tracking, respectively. Motivated by these, in this paper, we propose a large margin structured convolution operator (LMSCO) to achieve efficient TIR object tracking. To improve the tracking performance, we employ the spatial regularization and implicit interpolation to obtain continuous deep feature maps, including deep appearance features and deep motion features, of the TIR targets. Finally, a collaborative optimization strategy is exploited to significantly update the operators. Our approach not only inherits the advantage of the strong discriminative capability of SOSVM but also achieves accurate and robust tracking with higher-dimensional features and more dense samples. To the best of our knowledge, we are the first to incorporate the advantages of DCF and SOSVM for TIR object tracking. Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e. VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker achieves impressive results and outperforms most state-of-the-art trackers in terms of accuracy and robustness with sufficient frame rate.