VIENA2: A Driving Anticipation Dataset

Action anticipation is critical in scenarios where one needs to react before the action is finalized. This is, for instance, the case in automated driving, where a car needs to, e.g., avoid hitting pedestrians and respect traffic lights. While solutions have been proposed to tackle subsets of the driving anticipation tasks, by making use of diverse, task-specific sensors, there is no single dataset or framework that addresses them all in a consistent manner. In this paper, we therefore introduce a new, large-scale dataset, called VIENA2, covering 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. We discuss our data acquisition strategy and the statistics of our dataset, and benchmark state-of-the-art action anticipation techniques, including a new multi-modal LSTM architecture with an effective loss function for action anticipation in driving scenarios.Comment: Accepted in ACCV 201

Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset

Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems

Multispectral pedestrian detection is capable of adapting to insufficient illumination conditions by leveraging color-thermal modalities. On the other hand, it is still lacking of in-depth insights on how to fuse the two modalities effectively. Compared with traditional pedestrian detection, we find multispectral pedestrian detection suffers from modality imbalance problems which will hinder the optimization process of dual-modality network and depress the performance of detector. Inspired by this observation, we propose Modality Balance Network (MBNet) which facilitates the optimization process in a much more flexible and balanced manner. Firstly, we design a novel Differential Modality Aware Fusion (DMAF) module to make the two modalities complement each other. Secondly, an illumination aware feature alignment module selects complementary features according to the illumination conditions and aligns the two modality features adaptively. Extensive experimental results demonstrate MBNet outperforms the state-of-the-arts on both the challenging KAIST and CVC-14 multispectral pedestrian datasets in terms of the accuracy and the computational efficiency. Code is available at https://github.com/CalayZhou/MBNet

Multimodal fusion architectures for pedestrian detection

Pedestrian detection provides a crucial functionality in many human-centric applications, such as video surveillance, urban scene analysis, and autonomous driving. Recently, multimodal pedestrian detection has received extensive attention since the fusion of complementary information captured by visible and infrared sensors enables robust human target detection under daytime and nighttime scenes. In this chapter, we systematically evaluate the performance of different multimodal fusion architectures in order to identify the optimal solutions for pedestrian detection. We made two important observations: (1) it is useful to combine the most commonly used concatenation fusion scheme with a global scene-aware mechanism to learn both human-related features and correlation between visible and thermal feature maps; (2) the two-stream segmentation supervision without multimodal fusion provides the most effective scheme to infuse segmentation information as supervision for learning human-related features. Based on these studies, we present a unified multimodal fusion framework for joint training of target detection and segmentation supervision which achieves the state-of-the-art multimodal pedestrian detection performance on the public KAIST benchmark dataset.</p