Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.
MOT in urban traffic scenes was previously studied in @cite_13 , where the use of background subtraction is proposed for detecting the objects of interest followed by updating the object model with a state machine that uses feature points and spatial information. In fact, most previous work in MOT uses background subtraction or optical flow to detect the objects. The reason is that historically, methods based on pre-trained bounding box detectors are difficult to apply to road user tracking scenarios because it is difficult to design a detector that can detect and classify every possible type of road user from various viewpoints. However, recent progress in deep learning @cite_4 @cite_11 make this avenue now possible and worth investigating.
@cite_13 @cite_4 @cite_11
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Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving. In tracking-by-detection, a major challenge of online MOT is how to robustly associate noisy object detections on a new video frame with previously tracked objects. In this work, we formulate the online MOT problem as decision making in Markov Decision Processes (MDPs), where the lifetime of an object is modeled with a MDP. Learning a similarity function for data association is equivalent to learning a policy for the MDP, and the policy learning is approached in a reinforcement learning fashion which benefits from both advantages of offline-learning and online-learning for data association. Moreover, our framework can naturally handle the birth death and appearance disappearance of targets by treating them as state transitions in the MDP while leveraging existing online single object tracking methods. We conduct experiments on the MOT Benchmark [24] to verify the effectiveness of our method. In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis. The paper presents a moving object extraction method robust against illumination level changes for a real-time pedestrian counting system based on an x-t space-time image. This system, called the Ped-Counter, counts the number of pedestrians using TV camera images and image processing. The TV camera takes images of a path or the entrance of a store or an exhibition hall. A practical location for the counting system is under constant illumination conditions, such as under an arcade or in a room. If the system is used in other locations and under different conditions, for example, an outdoor path or an entrance where there is direct sunlight, then it must be robust against illumination level changes. The paper proposes two new processes for extracting moving objects from space-time images using background subtraction. The first is a background image reconstruction process using statistical characteristic analysis of temporal changes of a target pixel. The other is a moving object extraction process using delayed subtraction. These processes make the system robust against illumination level changes. This method is evaluated over a half-year period at an entrance experiencing drastic illumination level changes. We also confirm that this method can extract moving objects, that it is robust against season, weather, and time, and that the Ped-Counter is accurate. To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72 mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.