Wencheng Wu

Video-based real-time on-street parking occupancy detection system

Abstract. Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5  frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

Computer vision in roadway transportation systems: a survey

Abstract. There is a worldwide effort to apply 21st century intelligence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emission reduction. Computer vision is playing a key role in this transportation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges including imaging under a variety of environmental and illumination conditions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art.

Parking lot occupancy determination from lamp-post camera images

In recent years, detection of parking space availability has become of great importance worldwide due to its high correlation with fuel consumption and traffic congestion. We propose a novel framework for the automatic detection of vacant parking spaces from a lamp-post camera view of a parking lot. Our method models the 3-D volume parking spaces based on the parking lot geometry. The occupancy of the parking lot is determined based on a vehicle detector and the inferred volume of each space. We evaluate our method on three different datasets and show that its accuracy is close to 80% on a wide variety of test images.

Streak detection in mottled and noisy images

We describe a method for automatically detecting streaks in printed images using adaptive window-based image pro- jections and mutual information. The proposed approach accepts a scanned image enclosing the defect and computes the projections across the entire image at different window sizes. The resulting traces collected from the projections are analyzed with a peak de- tection algorithm and subsequently correlated using normalized mu- tual information to pinpoint the location and width of streak(s). Fi- nally, for a given peak, the window size is changed adaptively to identify and locate the intensity and length of the corresponding streak(s) while maximizing the signal-to-noise ratio. Results on syn- thetic and real-life images are provided to demonstrate the effective- ness of our proposed technique.

Perception-based line quality measurement

It is well-known that many sub-attributes of line quality, such as edge raggedness, line waviness, etc., contribute to the perception of the overall line quality. But the relative importance of these sub-attributes is not clear, nor is there a method available for combining them into one representative number for overall line quality. To address these issues, we have designed and conducted a series of psychophysical experiments, which explore the shape of the human visual transfer functions (VTF) relevant to the perception of these sub-attributes. Based on this, we have proposed an approach to assess overall line quality. In our method, we first pre-process the line image acquired (for example from a scanner) and extract certain profiles relevant to line quality measurement. A set of corresponding VTF’s is then applied to these profiles to calculate the various sub-attributes. Finally, overall line quality is determined by the weighted combination of these individual sub-attributes. Our preliminary results show that this measurement correlates well with human perception of overall line quality, for the sub-attributes studied.

A Mutual Information Based Automatic Registration and Analysis Algorithm for Defect Identification in Printed Documents

In this paper, we propose a defect analysis system, which automatically aligns a digitized copy of a printed output to a reference electronic original and subsequently illustrates potential image quality artifacts. We focus on image defects or artifacts caused by shortfalls in mechanical or electrophotographic processes. In this method, log-polar transform and mutual information techniques are used for image registration. A confidence map is then calculated by comparing the contrast and entropy of the neighborhood for each pixel in both images. This confidence map results in a qualitative difference between printed documents and electronic originals. The algorithm was demonstrated successfully on a database of 94 images with 95.7% accuracy.

Automated algorithm for the identification of artifacts in mottled and noisy images

We describe a method for automatically classifying image-quality defects on printed documents. The proposed approach accepts a scanned image where the defect has been localized a priori and performs several appropriate image processing steps to reveal the region of interest. A mask is then created from the exposed region to identify bright outliers. Morphological reconstruction techniques are then applied to emphasize relevant local attributes. The classification of the defects is accomplished via a customized tree classifier that utilizes size or shape attributes at corresponding nodes to yield appropriate binary decisions. Applications of this process include automated/assisted diagnosis and repair of printers/copiers in the field in a timely fashion. The proposed technique was tested on a database of 276 images of synthetic and real-life defects with 94.95% accuracy.

Monocular vision-based vehicular speed estimation from compressed video streams

This paper introduces a monocular vision-based vehicular speed estimation algorithm that operates in the compressed domain. The algorithm relies on the use of motion vectors associated with video compression to achieve computationally efficient and accurate speed estimation. Building the speed estimation directly into the compression step adds only a small amount of computation which is conducive to real-time performance. We demonstrate the effectiveness of the algorithm on 30 fps video of one hundred and forty vehicles travelling at speeds ranging from 30 to 60 mph. The average speed estimation accuracy of our algorithm across the test set was better than 2.50% at a yield of 100%, with the accuracy increasing as the yield decreases and as the frame rate increases.

Tire classification from still images and video

The use of different types of tires (e.g., all-season, snow, studded, summer) is regulated by law in several states and countries. Violation of tire usage laws typically results in substantial fines for infringers. In this paper, we propose an automated method to classify tires into snow, all-season and summer tires from still images or from a sequence of video frames. Our method first trains a Support Vector Machine (SVM) classifier on features extracted from a set of training images. Classification of test tire images is a two-stage process that entails feature extraction and tire classification based on the processing of the extracted features by the previously trained SVM classifier. The principle underlying the feature extraction stage is the representation of tire images via a low-dimensional approximation obtained from Principal Component Analysis (PCA). In order to improve robustness to changes in illumination and perspective, the features are extracted from the frequency representation of the binary edge map of the tire tread image. Our experimental results show that the proposed method achieves high classification accuracy.

Optimal patch code design via device characterization

In many color measurement applications, such as those for color calibration and profiling, “patch code” has been used successfully for job identification and automation to reduce operator errors. A patch code is similar to a barcode, but is intended primarily for use in measurement devices that cannot read barcodes due to limited spatial resolution, such as spectrophotometers. There is an inherent tradeoff between decoding robustness and the number of code levels available for encoding. Previous methods have attempted to address this tradeoff, but those solutions have been sub-optimal. In this paper, we propose a method to design optimal patch codes via device characterization. The tradeoff between decoding robustness and the number of available code levels is optimized in terms of printing and measurement efforts, and decoding robustness against noises from the printing and measurement devices. Effort is drastically reduced relative to previous methods because print-and-measure is minimized through modeling and the use of existing printer profiles. Decoding robustness is improved by distributing the code levels in CIE Lab space rather than in CMYK space.