As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
Object tracking in image sequences has become one of the pillars of artificial intelligence-based applications in the age of intelligent systems and video analytics in real time. The topic of tracking moving objects through a sequence of images is critical to AI applications, from the technology behind autonomous vehicles and smart monitoring to situational awareness, human behaviour analysis and advancing health diagnosis. Tracking objects seamlessly across images or in subsequent frames is vital to deliver reliable results, but doing so is not easy. Automated algorithms, even the best, fail in many situations, specifically with object occlusions, multiple similarly looking objects that require tracking, or having irregular movement models. This is where Human-in-the-Loop (HITL) can play an important role [1] .
HITL combines the speed of machine learning with the contextual understanding of human intelligence with attention to absolute detail. HITL enables detailed annotations, corrects mistakes, and allows continuous learning that can add significant value and improve the quality of the AI models and their reliability in the field. In this article, we take you through the complete process of annotating image sequences for tracking an object using HITL workflows.
The first thing you need to do with any annotation project is clarify goals and objectives. You should know what objects are to be tracked, the reason(s) for tracking, and how the annotated data will be applied. Once you have clarity, this will inform the rest of the process [2].
For example, in a healthcare setting, your goal may include tracking surgical tools so you can improve simulations for training purposes; in the retail environment, the goal could include tracking customer movement through a store layout for optimization or layout design purposes.
The goal of preparation is to provide the images or video—once they have the appropriate visibility and consistency. If the video data were poorly prepared prior to annotation, note that they may cause annotation errors and loss in model accuracy.
This preparation is important with long-duration videos, where the continuity of an object must be maintained across hundreds or thousands of frames.
In the effort of enlarging the annotation process, object tracking will almost always be started with an automatic method to avoid tedious valuable hours of manual and time-consuming work. Automated object detection and tracking methods such as YOLO, DeepSORT, ByteTrack, Kalman filters, can be effective considering speed, efficiency, and accuracy to label and track objects.
Despite the simplicity of tracking algorithms, they can produce problems including:
This is where the action is!! This is where human annotators come in to correct the errors that automated tools introduce. They do this by:
For trusted contexts, like with medical imaging or public safety, expert reviewers (e.g., doctors, surveillance analysts) confirm every single annotation.
Statswork uses a multi-level quality assurance process for every annotated series:
This twin quality assurance system ensures that tracking data satisfies the desired standard for high-quality AI training.
Once all annotations have been reviewed and approved, the data is exported in an acceptable format for your machine learning model. The common formats are:
AI models continue to change and so should your annotation process. Feedback on model performance, customer reviews, or edge cases should be incorporated into the annotation process again. Possible examples on how to incorporate this could include:
The cycle of changing the annotation process based on earlier work brings back relevance and thus maintains the integrity of your data over time [5].
Purely automated object tracking can be effective in straightforward scenarios. However, in the real world—where objects move unpredictably, lighting conditions vary, and occlusions are frequent—automated tools can fail. Human reviewers bring context, intuition, and domain-specific expertise that machines lack.
At Statswork, our HITL model integrates:
This hybrid approach ensures that your object tracking annotations are not just accurate but also aligned with real-world applications [4]
Accurate object tracking in image sequences is a critical enabler of advanced AI capabilities. But achieving it requires more than just technology—it demands thoughtful design, structured workflows, and human oversight. By leveraging human-in-the-loop annotation systems, you can ensure that your models are built on reliable, high-quality data that performs in dynamic, real-world environments.
Whether you’re developing smart retail systems, life-saving medical tools, or next-generation mobility solutions, Statswork offers tailored annotation workflows that combine cutting-edge automation with human excellence. Let us help you bring clarity and context to every frame.
Contact Statswork today to learn how our expert-guided, HITL-powered image sequence annotation services can enhance your AI models and accelerate your innovation journey.
