Object perception in autonomous cars and drones requires strong spatiotemporal integration. A new Spatiotemporal Feature Integration Network (SFIN) is introduced in this paper that can combine motion and appearance information to enable better perception. The two-stream method of SFIN checks the first stream, deriving spatial data from the optical flow of a video frame using a Convolutional Neural Network (CNN), and another stream, running in parallel as an auxiliary, deriving temporal data from optical flow by fusing a binarised 3D CNN. The two streams are then merged with a learnable attention model to dynamically control the relative weighting of spatial and temporal information based on scene context. We evaluate SFIN on the challenging Dynamic Urban Scene Perception (DUSP-455) benchmark, which comprises 455 complex video streams of object interactions. SFIN is applied to Python, uses OpenCV for optical flow computation and PyTorch for the deep model, and offers state-of-the-art detection quality and tracking performance at real-time speeds. This dramatic advance provides a strong perception in dynamic worlds.
C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM,” IEEE Trans. Robot., vol. 37, no. 6, pp. 1874-1890, 2021.
M. Gehrig, W. Aarents, D. Gehrig, and D. Scaramuzza, “DSEC: A stereo event camera dataset for driving scenarios,” IEEE Robot. Autom. Lett., vol. 6, no. 3, pp. 4947-4954, 2021.
B. Bescos, J. M. Fácil, J. Civera, and J. Neira, “DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 4076-4083, 2018.
V. S. A. Anala and S. Chintapalli, “Scalable data partitioning strategies for efficient query optimization in cloud data warehouses,” FMDB Transactions on Sustainable Computer Letters, vol. 2, no. 4, pp. 195-206, 2024.
T. Schöps, T. Sattler, and M. Pollefeys, “BAD SLAM: Bundle Adjusted Direct RGB-D SLAM,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, United States of America, 2019.
S. Jeyabal, W. Sachinthana, S. Bhagya, P. Samarakoon, M. R. Elara, and B.-J. Sheu, “Hard-to-Detect Obstacle Mapping by Fusing LiDAR and Depth Camera,” IEEE Sens. J., vol. 24, no. 15, pp. 24690-24698, 2024.
K. Yu, T. Tao, H. Xie, Z. Lin, T. Liang, B. Wang, P. Chen, D. Hao, Y. Wang, and X. Liang, “Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada, 2023.
K. Anitha, B. K. Nagaraj, P. Paramasivan, and T. Shynu, “Enhancing clustering performance with the rough set C-means algorithm,” FMDB Transactions on Sustainable Computing Systems, vol. 1, no. 4, pp. 190-203, 2023.
S. Klenk, J. Chui, N. Demmel, and D. Cremers, “TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021.
S. Khattak, C. Papachristos, and K. Alexis, “Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments,” in 2019 IEEE Aerospace Conference, Big Sky, MT, United States of America, 2019.
G. Gallego, T. Delbrück, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis, et al., “Event-based vision: A survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 1, pp. 154-180, 2020.
M. Ataseveri and B. Kose, “Sustainable transformation in logistics: Environmental innovations and human-centered strategies,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 4, pp. 201-210, 2024.
V. Attaluri, “Advanced data cleaning pipelines for high volume unstructured text datasets in real-time applications,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 4, pp. 209-218, 2024.
S. Banala, “The future of site reliability: Integrating generative AI into SRE practices,” FMDB Transactions on Sustainable Computer Letters, vol. 2, no. 1, pp. 14-25, 2024.
D. Femi, C. Satheesh, M. Sakthivanitha, R. Maruthi, G. Gnanaguru, and M. Paslavskyi, “Sustainable environmental optimization of abrasive water jet machining parameters of AZ31D/B₄C composite using TOPSIS technique,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 3, pp. 138-148, 2024.
A. Rosinol, M. Abate, Y. Chang, and L. Carlone, “Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020.
J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou, “LoFTR: Detector-free local feature matching with transformers,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Nashville, Tennessee, United States of America, 2021.
D. DeTone, T. Malisiewicz, and A. Rabinovich, “SuperPoint: Self-Supervised Interest Point Detection and Description,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, United States of America, 2018.
M. S. Islam, R. Islam, and M. S. H. Ridoy, “Investigating the design and performance optimization of spindle boxes in modular machine tools,” FMDB Transactions on Sustainable Energy Sequence, vol. 2, no. 2, pp. 60-87, 2024.
F. J. John Joseph, K. Chinnusamy, J. Jeganathan, A. J. Obaid, and S. S. Rajest, Eds., Machine Learning, Predictive Analytics, and Optimization in Complex Systems, IGI Global, USA, 2025, [Online]. Available: https://doi.org/10.4018/979-8-3373-5203-9.
S. Kannan, S. Paneerselvam, M. N. Saroja, and M. A. Ahmad, “Real-time air quality prediction and role in environmental protection using machine learning,” FMDB Transactions on Sustainable Environmental Sciences, vol. 2, no. 1, pp. 50-59, 2025.
R. S. Madhuranthakam, “Scalable data engineering pipelines for real-time analytics in big data environments,” FMDB Transactions on Sustainable Computing Systems, vol. 2, no. 3, pp. 154-166, 2024.
I. O. Mathew, I. Otaraku, A. Oji, and P. N. Ikenyiri, “Addressing methane venting: Strategies and implications on the environment,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 1, pp. 41-56, 2024.
M. S. Minu, S. S. Subashka Ramesh, R. Canessane, M. Al-Amin, and R. B. Sulaiman, “Experimental analysis of UAV networks using oppositional glowworm swarm optimization and deep learning clustering and classification,” FMDB Transactions on Sustainable Computing Systems, vol. 1, no. 3, pp. 124-134, 2023.
M. Mokdad, “Environmental strategic dynamics of global energy management and security,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 2, pp. 91-106, 2024.
S. Panyaram, “Integrating artificial intelligence with big data for real-time insights and decision-making in complex systems,” FMDB Transactions on Sustainable Intelligent Networks, vol. 1, no. 2, pp. 85-95, 2024.
S. Panyaram, “Optimization strategies for efficient charging station deployment in urban and rural networks,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 2, pp. 69-80, 2024.
P. Pulivarthy, “Optimizing large-scale distributed data systems using intelligent load balancing algorithms,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 4, pp. 219-230, 2024.
A. Raj, A. K. Gupta, R. K. Pachauri, and V. Sharma, “Performance evaluation of Walrus optimization-based MPPT with other algorithms under partial shading condition,” FMDB Transactions on Sustainable Energy Sequence, vol. 2, no. 2, pp. 88-101, 2024.
S. S. Rajest, S. Moccia, B. Singh, R. Regin, and J. Jeganathan, Eds., Advancing Intelligent Networks Through Distributed Optimization, Advances in Computer and Electrical Engineering, IGI Global, USA, Aug. 2024, [Online]. Available: https://doi.org/10.4018/979-8-3693-3739-4.
J. I. Ramos, R. Lacerona, and J. M. Nunag, “A study on operational excellence, work environment factors and the impact to employee performance,” FMDB Transactions on Sustainable Social Sciences Letters, vol. 1, no. 1, pp. 12-25, 2023.
P. Reena, G. Gnanaguru, S. B. V. J. Sara, D. Suresh, and H. K. Ibrahim, “Rheological and environmental performance of drilling muds enhanced with organic ash,” FMDB Transactions on Sustainable Applied Sciences, vol. 1, no. 2, pp. 99-112, 2024.
Y. M. Sabti, R. I. N. Alqatrani, M. I. Zaid, B. Taengkliang, and J. M. Kareem, “Impact of business environment on the performance of employees in the public-listed companies,” FMDB Transactions on Sustainable Management Letters, vol. 1, no. 2, pp. 56-65, 2023.
O. J. Singh, S. Mahapatra, S. R. Bose, A. Szeberényi, and C. C. Angelin, “A new distribution system power quality mitigation method using recursive least squares controlled dynamic voltage restorer,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 4, pp. 191-200, 2024.
A. Thirunagalingam, “Transforming real-time data processing: The impact of AutoML on dynamic data pipelines,” FMDB Transactions on Sustainable Intelligent Networks, vol. 1, no. 2, pp. 110-119, 2024.
R. Vani, K. Lalitha, S. B. V. J. Sara, V. Brindha, G. Gnanaguru, and D. Lale, “Real-time air traffic monitoring system with Raspberry Pi-based environmental monitoring,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 4, pp. 211-220, 2024.
E. Zanardo, “Chronostamp: A general-purpose run-time for data-flow computing in a distributed environment,” AVE Trends in Intelligent Computing Systems, vol. 1, no. 2, pp. 106-115, 2024.
A. S. Abdulbaqi, A. J. Obaid, and M. H. Abdulameer, “Smartphone-based ECG signals encryption for transmission and analyzing via IoMTs,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 24, no. 7, pp. 1979-1988, 2021.
S. R. Bose, J. A. Jeba, O. J. Singh, R. Regin, S. S. Rajest, and G. M. A. Sagayee, “Machine learning-based real-time stampede and crowd risk prediction,” AVE Trends in Intelligent Computer Letters, vol. 2, no. 1, pp. 53-66, 2026.
T. Kanimozhi and S. B. V. J. Sara, “Oppositional pufferfish optimization algorithm-based cluster head selection and congestion trust-aware energy-efficient clustering routing protocol in IoT-WSN,” FMDB Transactions on Sustainable Intelligent Networks, vol. 3, no. 1, pp. 26-44, 2026.
L. Y. Reddy, M. N. S. Sumanth, R. Regin, S. R. Bose, S. S. Jeev, and S. B. Sherine, “SECUREPATCH: Specialized multi-agent architecture with automated validation for security vulnerability repair,” AVE Trends in Intelligent Computing Systems, vol. 3, no. 1, pp. 23-47, 2026.
