Detection of vehicle occlusion using a generalized deformable model

This paper presents a vehicle occlusion detection algorithm based on a generalized deformable model. A 3D solid cuboid model with up to six vertices is employed to fit any vehicle images, by varying the vertices for a best fit. The advantage of using such a model is that the number of parameterized vertices is small which can be easily deformed. Occlusion is detected by recording the changes in the Area Ratio and the dimensions of the generalized deformable model. Our tests show that the new modeling algorithm is effective in detecting vehicle occlusion.published_or_final_versio

A Fast And Accurate Scoreboard Algorithm For estimating Stationary Backgrounds In An Image Sequence

This paper presents a stationary background estimation algorithm for color image sequence. The algorithm employs the running mode and running average algorithms, which are two commonly used algorithms, as the estimation core. A scoreboard is used to kept the pixel variations in the image sequence and is used to select between the running mode or the running average algorithm in each estimation. Our evaluation results show that by selecting, intelligently, the estimation core between the two algorithms according to the scoreboard values, the proposed background estimation algorithm has excellent performance in terms of estimation accuracy and speed.published_or_final_versio

Vehicle-type identification through automated virtual loop assignment and block-based direction biased motion estimation

This paper presents the concept of automated virtual loop assignment and loop-based motion estimation in vehicle-type identification. A major departure of our method from previous approaches is that the loops are automatically assigned to each lane; the size of virtual loops is much smaller for estimation accuracy; and the number of virtual loops per lane is large. Comparing this with traditional ILD, there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy and fully utilize computing resources. Second, there is no failure rate associated with the virtual loops and installation and maintenance cost can be kept to a minimum. Third, virtual loops may be re-allocated anywhere on the frame, giving flexibility in detecting different parameters.published_or_final_versio

An effective video analysis method for detecting red light runners

This paper presents a novel method for automatic red light runner detection on a video, which is fundamentally different from the concept of conventional red light camera systems. In principle, it extracts the state of the traffic lights and vehicle motions without any physical or electronic interconnections to the traffic light control system or the buried loop detectors. Purely from the video, the new method first constructs a traffic light sequence and then it estimates vehicle motions beyond the stop line while the light is red. In the former, the spatial and temporal relationships of individual traffic lights are utilized. In the latter, the concept of virtual loop detector has been introduced to emulate the physical loop detectors. A prototype was implemented based on this method and was tested in a number of field trials. The results show that the new method is able to detect multiple red light runners in multiple lanes. It is also capable of tolerating a number of hostile but realistic situation such as: 1) minimum number of traffic light; 2) pseudomotions due to shadows; 3) poor contrast; 4) pedestrian motions; and 5) turning vehicles.published_or_final_versio

Vehicle-Type Identification Through Automated Virtual Loop Assignment and Block-Based Direction-Biased Motion Estimation

This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that first, a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom (PTZ) actions without needing further human interaction. Second, the size of the virtual loops is much smaller for estimation accuracy. This enables the use of standard block-based motion estimation techniques that are well developed for video coding. Third, the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors (ILDs), there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy. Second, it may also be varied for an effective utilization of the computing resources. Third, there is no failure rate associated with the virtual loops or physical installation. As the loops are defined on the image sequence, changing the detection configuration or redeploying the loops to other locations on the same image sequence requires only a change of the assignment parameters. Fourth, virtual loops may be reallocated anywhere on the frame, giving flexibility in detecting different parameters. Our simulation results indicate that the proposed method is effective in type classification.published_or_final_versio

Real-time Aerial Vehicle Detection and Tracking using a Multi-modal Optical Sensor

Vehicle tracking from an aerial platform poses a number of unique challenges including the small number of pixels representing a vehicle, large camera motion, and parallax error. For these reasons, it is accepted to be a more challenging task than traditional object tracking and it is generally tackled through a number of different sensor modalities. Recently, the Wide Area Motion Imagery sensor platform has received reasonable attention as it can provide higher resolution single band imagery in addition to its large area coverage. However, still, richer sensory information is required to persistently track vehicles or more research on the application of WAMI for tracking is required. With the advancements in sensor technology, hyperspectral data acquisition at video frame rates become possible as it can be cruical in identifying objects even in low resolution scenes. For this reason, in this thesis, a multi-modal optical sensor concept is considered to improve tracking in adverse scenes. The Rochester Institute of Technology Multi-object Spectrometer is capable of collecting limited hyperspectral data at desired locations in addition to full-frame single band imagery. By acquiring hyperspectral data quickly, tracking can be achieved at reasonableframe rates which turns out to be crucial in tracking. On the other hand, the relatively high cost of hyperspectral data acquisition and transmission need to be taken into account to design a realistic tracking. By inserting extended data of the pixels of interest we can address or avoid the unique challenges posed by aerial tracking. In this direction, we integrate limited hyperspectral data to improve measurement-to-track association. Also, a hyperspectral data based target detection method is presented to avoid the parallax effect and reduce the clutter density. Finally, the proposed system is evaluated on realistic, synthetic scenarios generated by the Digital Image and Remote Sensing software