29 research outputs found
A fast multi-object tracking system using an object detector ensemble
Multiple-Object Tracking (MOT) is of crucial importance for applications such
as retail video analytics and video surveillance. Object detectors are often
the computational bottleneck of modern MOT systems, limiting their use for
real-time applications. In this paper, we address this issue by leveraging on
an ensemble of detectors, each running every f frames. We measured the
performance of our system in the MOT16 benchmark. The proposed model surpassed
other online entries of the MOT16 challenge in speed, while maintaining an
acceptable accuracy.Comment: 5 pages, 4 figures, 1 table, published in 2019 IEEE Colombian
Conference on Applications in Computational Intelligence (ColCACI
Simultaneous fusion, classification, andtraction of moving obstacles by LIDAR and camera using Bayesian algorithm
In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian
Spatial-Temporal Deep Embedding for Vehicle Trajectory Reconstruction from High-Angle Video
Spatial-temporal Map (STMap)-based methods have shown great potential to
process high-angle videos for vehicle trajectory reconstruction, which can meet
the needs of various data-driven modeling and imitation learning applications.
In this paper, we developed Spatial-Temporal Deep Embedding (STDE) model that
imposes parity constraints at both pixel and instance levels to generate
instance-aware embeddings for vehicle stripe segmentation on STMap. At pixel
level, each pixel was encoded with its 8-neighbor pixels at different ranges,
and this encoding is subsequently used to guide a neural network to learn the
embedding mechanism. At the instance level, a discriminative loss function is
designed to pull pixels belonging to the same instance closer and separate the
mean value of different instances far apart in the embedding space. The output
of the spatial-temporal affinity is then optimized by the mutex-watershed
algorithm to obtain final clustering results. Based on segmentation metrics,
our model outperformed five other baselines that have been used for STMap
processing and shows robustness under the influence of shadows, static noises,
and overlapping. The designed model is applied to process all public NGSIM
US-101 videos to generate complete vehicle trajectories, indicating a good
scalability and adaptability. Last but not least, the strengths of the scanline
method with STDE and future directions were discussed. Code, STMap dataset and
video trajectory are made publicly available in the online repository. GitHub
Link: shorturl.at/jklT0