Multiple Toddler Tracking in Indoor Videos

Multiple toddler tracking (MTT) involves identifying and differentiating toddlers in video footage. While conventional multi-object tracking (MOT) algorithms are adept at tracking diverse objects, toddlers pose unique challenges due to their unpredictable movements, various poses, and similar appearance. Tracking toddlers in indoor environments introduces additional complexities such as occlusions and limited fields of view. In this paper, we address the challenges of MTT and propose MTTSort, a customized method built upon the DeepSort algorithm. MTTSort is designed to track multiple toddlers in indoor videos accurately. Our contributions include discussing the primary challenges in MTT, introducing a genetic algorithm to optimize hyperparameters, proposing an accurate tracking algorithm, and curating the MTTrack dataset using unbiased AI co-labeling techniques. We quantitatively compare MTTSort to state-of-the-art MOT methods on MTTrack, DanceTrack, and MOT15 datasets. In our evaluation, the proposed method outperformed other MOT methods, achieving 0.98, 0.68, and 0.98 in multiple object tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and iterative and discriminative framework 1 (IDF1) metrics, respectively

Relationships among cell association, interference avoidance, and load balancing.

Relationships among cell association, interference avoidance, and load balancing.</p

Result of the objective function for four problem answers for different numbers of subbands.

Result of the objective function for four problem answers for different numbers of subbands.</p

Connection probability by changing the number of users in different distributions.

Connection probability by changing the number of users in different distributions.</p

The number of connections by changing the backhaul capacity of BSs.

The number of connections by changing the backhaul capacity of BSs.</p

Characteristics of the related research on user association, load balancing (LB), and inter-cell resource allocation.

Characteristics of the related research on user association, load balancing (LB), and inter-cell resource allocation.</p

Jain’s fairness for load factor by changing the number of users.

Jain’s fairness for load factor by changing the number of users.</p

Network utility by changing the backhaul capacity of the BSs.

Network utility by changing the backhaul capacity of the BSs.</p

Average number of user connections for each BS in each tier.

Average number of user connections for each BS in each tier.</p

Handwritten Logic Circuits Analysis Using the YOLO Network and a New Boundary Tracking Algorithm

Handwriting analysis has been addressed by researchers for decades, and many advances were achieved in understanding handwritten texts so far. However, some applications have been rarely discussed. One of these applications that has received less attention is the understanding and analyzing of handwritten circuits. Today, with the widespread use of intelligent tools in engineering and educational processes, the need for new and accurate solutions for processing such handwritings is felt more than ever. This paper presents a new method to analyze handwritten logic circuits. In this method, circuit components are first identified using a deep neural network based on YOLO. Then, the connection among these components is recognized using a new simple boundary tracking method. Then, the binary function related to the handwritten circuit is obtained. Finally, the truth table of the logic circuit is generated. We have also created a set of various handwritten logic circuits called JSU-HWLC. The results of the experiments show the proper performance of the proposed method on the collected dataset. The experiments demonstrated that the YOLO algorithm achieved better results than other deep learning methods such as faster R-CNN, Detectron2, and RetinaNet. For this reason, YOLO has been used to identify logic gates in the proposed system