zhengthomastang
commented
6 months ago Thank you for reaching out and for your keen interest in our work at the 2nd AI City Challenge Workshop in CVPR 2018. It's gratifying to know that our approach to integrating visual and semantic features for vehicle tracking and speed estimation has caught your attention. I'm more than happy to provide insights into your queries.
Robustness in Diverse Conditions: The system's performance under varying weather conditions and times of the day is a critical aspect of vehicle tracking. To maintain high accuracy, we recommend implementing dynamic thresholding and adaptive filtering techniques. These can help the system adjust to different lighting and weather conditions. Additionally, utilizing robust feature extractors that are less sensitive to environmental changes can be beneficial. Regular updates and recalibrations of the model based on recent environmental data may also be necessary.
Scalability to Larger Camera Networks: When scaling to a larger network of cameras, the main challenges are often related to data management and the increased computational load. To address these, efficient data streaming and processing techniques should be employed. Implementing a distributed processing system can help manage the increased data flow. It's also essential to refine the algorithms for data association to handle the complexities of a larger network while minimizing errors in vehicle identification and tracking across cameras.
Real-time Processing Capabilities: The real-time processing capabilities largely depend on the hardware infrastructure and the efficiency of the algorithms used. For real-time performance, high-speed processors and GPUs are recommended. Optimizing the code for parallel processing and reducing the computational complexity of algorithms wherever possible can also enhance real-time processing capabilities.
Adaptation to Newer Models: Integrating advanced object detectors like YOLOv4 involves fine-tuning these models to work cohesively with the existing framework. This includes aligning the input-output specifications and ensuring that the new models can efficiently process the data formats used in our system. Regular updates and optimizations may be required to maintain compatibility and performance.
Semantic Feature Enhancement: Incorporating additional semantic features could certainly enhance tracking accuracy and speed estimation. Considering elements such as vehicle behavior patterns, driver profiles, and contextual environmental data could provide a more holistic view, leading to improved performance. Integrating data from IoT devices and smart city infrastructure might also offer additional insights for more accurate tracking and analysis.
We're glad that our work could inspire your project, and we're excited about the potential advancements your adaptations might bring. Please feel free to reach out if you have further questions or need more detailed technical guidance.
Dear Zheng (Thomas) Tang and Team,
I hope this message finds you well. I am reaching out to you after a thorough examination of your repository, which contains the source code for the winning entries of Track 1 and Track 3 at the 2nd AI City Challenge Workshop in CVPR 2018. The fusion of visual and semantic features for single-camera and inter-camera vehicle tracking, as well as 3D speed estimation, is indeed a remarkable achievement.
As I delve deeper into the intricacies of your approach, I am particularly intrigued by the methodology employed in the semantic feature integration for vehicle tracking. The utilisation of DCNN features, license plates, detected car types, and travelling time information to compute the cost function in ICT is a testament to the sophistication of your system.
I am considering adapting your system for a project that requires high-accuracy vehicle tracking in a multi-camera setup. However, I am curious about the following aspects:
Robustness in Diverse Conditions: How does the system perform under varying weather conditions and during different times of the day? Are there any pre-processing steps or model adjustments that you would recommend to maintain high accuracy?
Scalability to Larger Camera Networks: What are the limitations when scaling the system to a larger network of cameras, and how might one address potential challenges in data association across an expanded camera network?
Real-time Processing Capabilities: Could you provide insights into the real-time processing capabilities of your system? Are there any particular hardware requirements or optimisations that are critical for achieving real-time performance?
Adaptation to Newer Models: With the advent of YOLOv4 and other advanced object detectors, what would be the recommended approach to integrate these newer models into your existing framework?
Semantic Feature Enhancement: Are there additional semantic features or data sources that you believe could further enhance the tracking accuracy or speed estimation performance?
I would greatly appreciate your insights on these matters. Your expertise and experience would be invaluable in guiding the adaptation of your system to meet the specific requirements of my project.
Thank you for your time and consideration. I look forward to your response.
Best regards, yihong1120