17,755 research outputs found
Long-Term Visual Object Tracking Benchmark
We propose a new long video dataset (called Track Long and Prosper - TLP) and
benchmark for single object tracking. The dataset consists of 50 HD videos from
real world scenarios, encompassing a duration of over 400 minutes (676K
frames), making it more than 20 folds larger in average duration per sequence
and more than 8 folds larger in terms of total covered duration, as compared to
existing generic datasets for visual tracking. The proposed dataset paves a way
to suitably assess long term tracking performance and train better deep
learning architectures (avoiding/reducing augmentation, which may not reflect
real world behaviour). We benchmark the dataset on 17 state of the art trackers
and rank them according to tracking accuracy and run time speeds. We further
present thorough qualitative and quantitative evaluation highlighting the
importance of long term aspect of tracking. Our most interesting observations
are (a) existing short sequence benchmarks fail to bring out the inherent
differences in tracking algorithms which widen up while tracking on long
sequences and (b) the accuracy of trackers abruptly drops on challenging long
sequences, suggesting the potential need of research efforts in the direction
of long-term tracking.Comment: ACCV 2018 (Oral
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection
In this paper, we propose to combine detections from background subtraction
and from a multiclass object detector for multiple object tracking (MOT) in
urban traffic scenes. These objects are associated across frames using spatial,
colour and class label information, and trajectory prediction is evaluated to
yield the final MOT outputs. The proposed method was tested on the Urban
tracker dataset and shows competitive performances compared to state-of-the-art
approaches. Results show that the integration of different detection inputs
remains a challenging task that greatly affects the MOT performance
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
We study unsupervised video representation learning that seeks to learn both
motion and appearance features from unlabeled video only, which can be reused
for downstream tasks such as action recognition. This task, however, is
extremely challenging due to 1) the highly complex spatial-temporal information
in videos; and 2) the lack of labeled data for training. Unlike the
representation learning for static images, it is difficult to construct a
suitable self-supervised task to well model both motion and appearance
features. More recently, several attempts have been made to learn video
representation through video playback speed prediction. However, it is
non-trivial to obtain precise speed labels for the videos. More critically, the
learnt models may tend to focus on motion pattern and thus may not learn
appearance features well. In this paper, we observe that the relative playback
speed is more consistent with motion pattern, and thus provide more effective
and stable supervision for representation learning. Therefore, we propose a new
way to perceive the playback speed and exploit the relative speed between two
video clips as labels. In this way, we are able to well perceive speed and
learn better motion features. Moreover, to ensure the learning of appearance
features, we further propose an appearance-focused task, where we enforce the
model to perceive the appearance difference between two video clips. We show
that optimizing the two tasks jointly consistently improves the performance on
two downstream tasks, namely action recognition and video retrieval.
Remarkably, for action recognition on UCF101 dataset, we achieve 93.7% accuracy
without the use of labeled data for pre-training, which outperforms the
ImageNet supervised pre-trained model. Code and pre-trained models can be found
at https://github.com/PeihaoChen/RSPNet.Comment: Accepted by AAAI-2021. Code and pre-trained models can be found at
https://github.com/PeihaoChen/RSPNe
Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications
Compute and memory demands of state-of-the-art deep learning methods are
still a shortcoming that must be addressed to make them useful at IoT
end-nodes. In particular, recent results depict a hopeful prospect for image
processing using Convolutional Neural Netwoks, CNNs, but the gap between
software and hardware implementations is already considerable for IoT and
mobile edge computing applications due to their high power consumption. This
proposal performs low-power and real time deep learning-based multiple object
visual tracking implemented on an NVIDIA Jetson TX2 development kit. It
includes a camera and wireless connection capability and it is battery powered
for mobile and outdoor applications. A collection of representative sequences
captured with the on-board camera, dETRUSC video dataset, is used to exemplify
the performance of the proposed algorithm and to facilitate benchmarking. The
results in terms of power consumption and frame rate demonstrate the
feasibility of deep learning algorithms on embedded platforms although more
effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
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Computational Modeling of Human Dorsal Pathway for Motion Processing
Reliable motion estimation in videos is of crucial importance for background iden- tification, object tracking, action recognition, event analysis, self-navigation, etc. Re- constructing the motion field in the 2D image plane is very challenging, due to variations in image quality, scene geometry, lighting condition, and most importantly, camera jit- tering. Traditional optical flow models assume consistent image brightness and smooth motion field, which are violated by unstable illumination and motion discontinuities that are common in real world videos.
To recognize observer (or camera) motion robustly in complex, realistic scenarios, we propose a biologically-inspired motion estimation system to overcome issues posed by real world videos. The bottom-up model is inspired from the infrastructure as well as functionalities of human dorsal pathway, and the hierarchical processing stream can be divided into three stages: 1) spatio-temporal processing for local motion, 2) recogni- tion for global motion patterns (camera motion), and 3) preemptive estimation of object motion. To extract effective and meaningful motion features, we apply a series of steer- able, spatio-temporal filters to detect local motion at different speeds and directions, in a way that\u27s selective of motion velocity. The intermediate response maps are cal- ibrated and combined to estimate dense motion fields in local regions, and then, local motions along two orthogonal axes are aggregated for recognizing planar, radial and circular patterns of global motion. We evaluate the model with an extensive, realistic video database that collected by hand with a mobile device (iPad) and the video content varies in scene geometry, lighting condition, view perspective and depth. We achieved high quality result and demonstrated that this bottom-up model is capable of extracting high-level semantic knowledge regarding self motion in realistic scenes.
Once the global motion is known, we segment objects from moving backgrounds by compensating for camera motion. For videos captured with non-stationary cam- eras, we consider global motion as a combination of camera motion (background) and object motion (foreground). To estimate foreground motion, we exploit corollary dis- charge mechanism of biological systems and estimate motion preemptively. Since back- ground motions for each pixel are collectively introduced by camera movements, we apply spatial-temporal averaging to estimate the background motion at pixel level, and the initial estimation of foreground motion is derived by comparing global motion and background motion at multiple spatial levels. The real frame signals are compared with those derived by forward predictions, refining estimations for object motion. This mo- tion detection system is applied to detect objects with cluttered, moving backgrounds and is proved to be efficient in locating independently moving, non-rigid regions.
The core contribution of this thesis is the invention of a robust motion estimation system for complicated real world videos, with challenges by real sensor noise, complex natural scenes, variations in illumination and depth, and motion discontinuities. The overall system demonstrates biological plausibility and holds great potential for other applications, such as camera motion removal, heading estimation, obstacle avoidance, route planning, and vision-based navigational assistance, etc
Vehicle Tracking and Speed Estimation from Traffic Videos
The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer vision approaches to propose an efficient way to predict vehicle speed. In this paper, we introduce some state-of-the-art approaches in vehicle speed estimation, vehicle detection, and object tracking, as well as our solution for Track 1 of the Challenge
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