When comparing traditional visitor counting solutions with DISPL AI-based traffic analysis, it is essential to understand their fundamental differences, strengths, and limitations. While both solutions aim to analyze visitor traffic, methodologies and the insights they provide vary significantly.

Key Differences and Technological Approach

1) Camera Angle & Occlusion Handling

Traditional visitor counters are typically installed overhead, offering a clear top-down perspective. This setup eliminates occlusion issues and ensures all movements within the defined area are detected. However, these counters cannot analyze visitor behavior, facial attributes, or differentiate between individuals, as they only track movement crossing a virtual threshold.

DISPL, in contrast, operates at an angled perspective, enabling advanced tracking of visitor behavior and demographic attributes. However, this introduces occlusion challenges, where overlapping individuals may temporarily obscure visibility. To counteract this, DISPL integrates a re-identification algorithm, ensuring unique visitors are recognized after temporary occlusions. Traditional ceiling counters lack such re-identification capabilities, meaning they do not distinguish unique visitors and simply count every crossing motion.

2) Placement & Counting Accuracy

Ceiling-mounted counters are positioned at entry/exit points, ensuring all visitors are detected at least once. However, since visitors cross these points twice per visit (entry and exit), the system often overestimates unique visitor counts. Additionally, these counters lack tracking beyond the entry point, making them ineffective for analyzing visitor movement patterns within a space.

DISPL's SPOT ANALYSIS approach enables tracking at multiple points within an environment, rather than just at the entrance. This allows for a deeper understanding of visitor movement patterns and time spent in different areas. However, because visitors may appear in the frame multiple times, DISPL requires re-identification algorithms to ensure accurate counting of unique visitors. Despite these challenges, DISPL achieves 70% to 95% accuracy in visitor tracking, depending on installation placement and traffic density.

Customer Journey Feature is in Beta and not ready yet.

3) Traffic Density Handling

A top-down view provides a straightforward count of people, but it does not distinguish unique individuals—it merely tracks objects moving through a designated area. In high-density settings, ceiling counters continue to detect movement reliably, but they do not differentiate individuals, leading to duplicate counts.

DISPL, with its angled view, faces potential occlusion challenges in dense crowds. However, with high-performance computing and continuous algorithm improvements, it can effectively re-identify visitors, ensuring more precise traffic analysis than traditional counters.

This image is an illustration of what DISPL algorithm can theoretically analyze when sensor is tuned perfectly and connected to powerful hardware. This is not a recommended use case.

4) Traffic Speed & Motion Handling

Traditional counters function by detecting motion across an entrance, meaning they do not account for visitor speed or movement patterns beyond this point. They simply register +1 on entry and -1 on exit, offering only basic presence detection.

DISPL, on the other hand, tracks individuals dynamically within a space. However, faster-moving individuals can introduce tracking challenges, particularly in environments with irregular movement patterns or sudden occlusions. To address this, DISPL optimizes frame rate processing and motion prediction algorithms, significantly enhancing accuracy in dynamic environments.

5) Distance from Sensor & Face Detection Precision

Ceiling-mounted counters capture all individuals at a fixed, consistent size since they are always viewed from above. This eliminates variability in detection accuracy but also prevents any form of detailed visitor analysis.

DISPL operates at an angled view, meaning detection accuracy varies depending on an individual’s distance from the sensor. For reliable detection, a minimum face size of 80 pixels is required. Visitors further away may not be detected with high accuracy. Despite this limitation, DISPL provides significantly richer data insights by enabling demographic analysis, behavioral tracking, and unique visitor identification.

Comparison of Traditional Ceiling-Mounted Counters vs. DISPL AI-Based Traffic Analysis

Conclusion

• Traditional visitor counters are simple, reliable, and work well for basic foot traffic counting. They are effective for detecting movement at entry points, but they do not differentiate unique visitors, track movement beyond the entrance, or provide demographic insights. Their advantage lies in their simplicity and ease of installation, but they provide only approximate traffic estimates without deeper analytics.
• DISPL AI-based analysis offers more precise visitor analytics, distinguishing unique individuals, analyzing behavior, and providing richer data insights. While occlusion, motion speed, and distance variations can affect accuracy, ongoing improvements in AI re-identification, motion tracking, and occlusion handling ensure DISPL achieves accuracy rates between 70% and 95%, depending on installation conditions and crowd density.

Final Verdict

Both solutions have their goals, benefits and disadvantages. They can't substitute each other and should be used together for different goals.

If the goal is simply counting visitor entries and exits, traditional traffic counters should be used. DISPL can't reliably count traffic.

However, for businesses that require deeper audience analytics, accurate unique visitor tracking, and behavioral and demographic insights, DISPL is the ideal choice. And traffic counters can't be used for these use cases.