Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. Here we present a comprehensive survey on recent advances in pedestrian detection. First, we provide a detailed review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches. For handcrafted features based methods, we present an extensive review of approaches and find that handcrafted features with large freedom degrees in shape and space have better performance. In the case of deep features based approaches, we split them into pure CNN based methods and those employing both handcrafted and CNN based features. We give the statistical analysis and tendency of these methods, where feature enhanced, part-aware, and post-processing methods have attracted main attention. In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance. Furthermore, we introduce some related datasets and evaluation metrics, and a deep experimental analysis. We conclude this survey by emphasizing open problems that need to be addressed and highlighting various future directions. Researchers can track an up-to-date list at https://github.com/JialeCao001/PedSurvey .

Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects (esp. small objects) is far from satisfying the demand of practical systems. Large-scale, rich-diversity, and high-resolution datasets play an important role in developing better object detection methods to satisfy the demand. Existing public large-scale datasets such as MS COCO collected from websites do not focus on the specific scenarios. Moreover, the popular datasets (e.g., KITTI and Citypersons) collected from the specific scenarios are limited in the number of images and instances, the resolution, and the diversity. To attempt to solve the problem, we build a diverse high-resolution dataset (called TJU-DHD). The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labeled objects in total with a large variance in scale and appearance. Meanwhile, the dataset has a rich diversity in season variance, illumination variance, and weather variance. In addition, a new diverse pedestrian dataset is further built. With the four different detectors (i.e., the one-stage RetinaNet, anchor-free FCOS, two-stage FPN, and Cascade R-CNN), experiments about object detection and pedestrian detection are conducted. We hope that the newly built dataset can help promote the research on object detection and pedestrian detection in these two scenes. The dataset is available at https://github.com/tjubiit/TJU-DHD.

Object detection methods based on Convolution Neural Networks (CNN) usually utilize feature pyramid networks to detect objects with various scales. The state-of-the-art feature pyramid networks improve detection accuracy by enhancing multi-level feature representations. Fusing multi-level features is the most effective manner to enhance feature representations. However, the existing feature pyramid networks usually fuse multi-level features by element-wise operations. It leads to the lack of long-range dependencies in the feature fusion. To address this problem, we propose a simple yet efficient feature pyramid network named latent feature pyramid network (LFPN). LFPN can enhance feature representations by modeling inner-scale and cross-scale long-range dependencies through conducting inner-scale and cross-scale feature fusion in the latent space. Comprehensive experiments are performed on two challenge object detection datasets: MS COCO and Pascal VOC. The experimental results show consistent improvements on various feature pyramid networks, backbones, and object detectors, which demonstrates the effectiveness and generality of our LFPN. Our code and models will be made publicly available.

Existence of haze significantly degrades visual quality and hence negatively affects the performance of visual surveillance, video analysis, and human-machine interaction. To remove haze from a visual signal, in this paper, we propose a generative adversarial network for visual haze removal called HRGAN. HRGAN consists of a generator network and a discriminator network. A unified network jointly estimating transmission maps, atmospheric light, and haze-free images (called UNTA) is proposed as the generator network of HRGAN. Instead of being optimized by minimizing the pixel-wise loss, HRGAN is optimized by minimizing a novel loss function consisting of pixel-wise loss, perceptual loss, and adversarial loss produced by a discriminator network. Classical model-based image dehazing algorithms consist of three separate stages: 1) estimating transmission map; 2) estimating atmospheric light; and 3) restoring haze-free image by using an atmospheric scattering model to process the transmission map and atmospheric light. Such a separate scheme is not guaranteed to achieve optimal results. On the contrary, UNTA performs transmission map estimation and atmospheric light estimation simultaneously to obtain joint optimal solutions. The experimental results on both synthetic and real-world image databases demonstrate that HRGAN outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency.

Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we propose the occlusion-sensitive hard example mining method and occlusion-sensitive loss that mines hard samples according to the occlusion level and assigns higher weights to the detection errors occurring at highly occluded pedestrians. Third, we empirically demonstrate that weak box-based segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons, Caltech and ETH datasets. Our approach sets a new state-of-the-art on all three datasets. Our approach obtains an absolute gain of 10.3% in log-average miss rate, compared with the best reported results on the heavily occluded HO pedestrian set of the CityPersons test set. Code and models are available at: https://github.com/Leotju/MGAN.

RGB-infrared object detection in remote sensing images is crucial for achieving around-clock surveillance of unmanned aerial vehicles. RGB-infrared remote sensing object detection methods based on deep learning usually mine the complementary information from RGB and infrared modalities by utilizing feature aggregation to achieve robust object detection for around-the-clock applications. Most of the existing methods aggregate features from RGB and infrared images by utilizing element-wise operations ( eg., element-wise addition or concatenation). The detection accuracy of these methods is limited. The main reasons can be concluded as follows: local location misalignment across modalities and insufficient non-local contextual information extraction. To address the above issues, we propose a cross-modal local calibration and global context modeling network (CLGNet), consisting of two novel modules: a cross-modal local calibration (CLC) module and a cross-modal global context modeling (CGC) module. The CLC module first aligns features from different modalities, and then aggregates them selectively. The CGC module is embedded into the backbone network to capture cross-modal non-local long-range dependencies. Experimental results on popular RGB-infrared remote sensing object detection datasets, namely DRoneVehicle and VEDAI demonstrate the effectiveness and efficiency of our CLGNet.